Probability and statistics Books
Springer Nature Switzerland AG Integrating Data Science and Earth Science:
Book SynopsisThis open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.Table of ContentsData Science and Earth System Science.- The Digital Earth project: focus and agenda.- Data analysis and exploration with visual approaches.- Data analysis and exploration with computational approaches.- Data analysis and exploration with scientific workflows.- The Digital Earth SMART monitoring concept and tools.- Interdisciplinary collaboration.- Evaluating the success of the Digital Earth project.- Lessons learned in the Digital Earth project.
£23.74
Springer International Publishing AG Introduction to Mathematics for Economics with R
Book SynopsisThis book provides a practical introduction to mathematics for economics using R software. Using R as a basis, this book guides the reader through foundational topics in linear algebra, calculus, and optimization. The book is organized in order of increasing difficulty, beginning with a rudimentary introduction to R and progressing through exercises that require the reader to code their own functions in R. All chapters include applications for topics in economics and econometrics. As fully reproducible book, this volume gives readers the opportunity to learn by doing and develop research skills as they go. As such, it is appropriate for students in economics and econometrics.Table of Contents1. Introduction to R.- 2. Linear Algebra.- 3. Functions of one variable.- 4. Dierential Calculus.- 5. Integral Calculus.- 6. Multivariable Calculus.- 7. Constrained Optimization.- 8. Trigonometry.- 9. Complex numbers.- 10. Difference equations.- 11. Differential equations.
£42.74
Springer International Publishing AG Numerical Methods for Solving Discrete Event
Book SynopsisThis graduate textbook provides an alternative to discrete event simulation. It describes how to formulate discrete event systems, how to convert them into Markov chains, and how to calculate their transient and equilibrium probabilities. The most appropriate methods for finding these probabilities are described in some detail, and templates for efficient algorithms are provided. These algorithms can be executed on any laptop, even in cases where the Markov chain has hundreds of thousands of states. This book features the probabilistic interpretation of Gaussian elimination, a concept that unifies many of the topics covered, such as embedded Markov chains and matrix analytic methods.The material provided should aid practitioners significantly to solve their problems. This book also provides an interesting approach to teaching courses of stochastic processes. Trade Review“This monograph is an exciting addition to the queueing/stochastic processes literature, written by two highly respected senior researchers. … The writing is precise and clear. Well-known models are used as examples to illustrate the methods presented. … It has a huge number of powerful techniques that are not given sufficient focus elsewhere. This may be one of the best books to introduce graduate students … . This monograph is essential for the bookshelf … of every serious queueing theorist.” (Myron Hlynka, Mathematical Reviews, December, 2023)Table of ContentsBasic Concepts and Definitions.- Systems with Events Generated by Poisson or by Binomial Processes.- Generating the Transition Matrix.- Systems with Events Created by Renewal Processes.- Systems with Events Created by Phase-type Processes.- Computational Complexity and Rounding and Truncation Errors.- Transient Solutions of Markov Chains.- Moving Toward the Statistical Equilibrium.- Equilibrium Solutions of Markov Chains and Related Topics.- Reducing the State Space Through Censoring and Embedding.- Systems with Independent or Almost Independent Components.- Infinite-State Markov Chains and Matrix Analytic Methods.
£67.49
Springer International Publishing AG Innovations in Multivariate Statistical Modeling:
Book SynopsisMultivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics.It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty.Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.Table of ContentsPreface.- PART 1: Trends in Multi- and Matrix-Variate Analysis.- Q. Guo, X. Deng and N. Ravishanker: Association-based Optimal Subpopulation Selection of Multivariate Data.- T. B. Mattos, L. A. Matos, V. H Lachos Aldo: Likelihood-Based Inference For Linear Mixed-Effects Models With Censored Response Using Skew-Normal Distribution.- Y. Melnykov, M. Perry, V. Melnykov: Robust Estimation of Multiple Change Points in Multivariate Processes.- T. Botha, J. T Ferreira and A. Bekker: Some Computational Aspects Of A Noncentral Dirichlet Family.- Y. Murat Bulut and Olcay Arslan: Modeling Handwritten Digits Dataset Using The Matrix Variate T Distribution.- B. Byukusenge, D. von Rosen and M. Singull: On The Identification Of Extreme Elements In A Residual For The Gmanova-Manova Model.- M. Billio, R. Casarin, M. Costola and M. Iacopini: Matrix-variate Smooth Transition Models for Temporal Networks.- H. Baghishani and J. Ownuk: A Flexible Matrix-Valued Response Regression For Skewed Data.- J. Trink, H. Haghbin and M. Maadooliat: Multivariate Functional Singular Spectrum Analysis: A Nonparametric Approach for Analyzing Functional Time Series.- M. Greenacre: Compositional Data Analysis — Linear Algebra, Visualization And Interpretation.- A. Alzaatreh, F. Famoye and C. Lee: Multivariate Count Data Regression Models And Their Applications.- A. Iranmanesh, M. Rafiei and D. Nagar: A Generalized Multivariate Gamma Distribution.- PART 2: Aspects of High Dimensional Methodology and Bayesian Learning .- G. D' Angella and C. Hennig: A Comparison Of Different Clustering Approaches For High-Dimensional Presence-Absence Data.- S. Millard, M. Arashi and G. Maribe: High-Dimensional Feature Selection For Logistic Regression Using Blended Penalty Functions.- I. Munaweera, S. Muthukumarana and M. Jafari Jozani: A Generalized Quadratic Garrote Approach Towards Ridge Regression Analysis.- M. Roozbeh: High Dimensional Nonlinear Optimization Problem In Semiparametric Regression Model.- PART 3: Frontiers in Robust Analysis and Mixture Modelling.- A. Punzo and S. D. Tomarchia: Parsimonious Finite Mixtures Of Matrix-Variate Regressions.- F. Zehra Doğru and Olcay Arslan:Robust Multivariate Modelling for Heterogeneous Data Sets With Mixtures of Multivariate Skew Laplace Normal Distributions.- M. Norouzirad, M. Arashi, F. J Marques and F. Esmaeili: Robust Estimation Through Preliminary Testing Based On The Lad-Lasso.
£132.99
Springer International Publishing AG Measure Theory, Probability, and Stochastic
Book SynopsisThis textbook introduces readers to the fundamental notions of modern probability theory. The only prerequisite is a working knowledge in real analysis. Highlighting the connections between martingales and Markov chains on one hand, and Brownian motion and harmonic functions on the other, this book provides an introduction to the rich interplay between probability and other areas of analysis.Arranged into three parts, the book begins with a rigorous treatment of measure theory, with applications to probability in mind. The second part of the book focuses on the basic concepts of probability theory such as random variables, independence, conditional expectation, and the different types of convergence of random variables. In the third part, in which all chapters can be read independently, the reader will encounter three important classes of stochastic processes: discrete-time martingales, countable state-space Markov chains, and Brownian motion. Each chapter ends with a selection of illuminating exercises of varying difficulty. Some basic facts from functional analysis, in particular on Hilbert and Banach spaces, are included in the appendix. Measure Theory, Probability, and Stochastic Processes is an ideal text for readers seeking a thorough understanding of basic probability theory. Students interested in learning more about Brownian motion, and other continuous-time stochastic processes, may continue reading the author’s more advanced textbook in the same series (GTM 274).Table of ContentsPart I. Measure Theory.- Chapter 1. Measurable Spaces.- Chapter 2. Integration of Measurable Functions.- Chapter 3. Construction of Measures.- Chapter 4. Lp Spaces.- Chapter 5. Product Measure.- Chapter 6. Signed Measures.- Chapter 7. Change of Variables.- Part II. Probability Theory.- Chapter 8. Foundations of Probability Theory.- Chapter 9. Independence.- Chapter 10. Convergence of Random Variables.- Chapter 11. Conditioning.- Part III. Stochastic Processes.- Chapter 12. Theory of Martingales.- Chapter 13. Markov Chains.- Chapter 14. Brownian Motion.
£999.99
Springer International Publishing AG Stochastic Processes, Statistical Methods, and
Book SynopsisThe goal of the 2019 conference on Stochastic Processes and Algebraic Structures held in SPAS2019, Västerås, Sweden, from September 30th to October 2nd 2019, was to showcase the frontiers of research in several important areas of mathematics, mathematical statistics, and its applications. The conference was organized around the following topics1. Stochastic processes and modern statistical methods,2. Engineering mathematics,3. Algebraic structures and their applications.The conference brought together a select group of scientists, researchers, and practitioners from the industry who are actively contributing to the theory and applications of stochastic, and algebraic structures, methods, and models. The conference provided early stage researchers with the opportunity to learn from leaders in the field, to present their research, as well as to establish valuable research contacts in order to initiate collaborations in Sweden and abroad. New methods for pricing sophisticated financial derivatives, limit theorems for stochastic processes, advanced methods for statistical analysis of financial data, and modern computational methods in various areas of applied science can be found in this book. The principal reason for the growing interest in these questions comes from the fact that we are living in an extremely rapidly changing and challenging environment. This requires the quick introduction of new methods, coming from different areas of applied science. Advanced concepts in the book are illustrated in simple form with the help of tables and figures. Most of the papers are self-contained, and thus ideally suitable for self-study. Solutions to sophisticated problems located at the intersection of various theoretical and applied areas of the natural sciences are presented in these proceedings. Table of ContentsPart I. Stochastic Processes.- Chapter 1. Albuhayri, M., Engström, C., Malyarenko, A., Ni, Y., Silvestrov, S.: An improved asymptotics of implied volatility in the Gatheral model.- Chapter 2. Jamsher Ali, M., Pärna, K.: Ruin probability for merged risk processes with correlated arrivals.- Chapter 3. Nwe Aye, T., Carlsson, L.: Method Development for Emergent Properties in Stage-Structured Population Models with Stochastic Resource Growth.- Chapter 4. Golomoziy, V.: Computable bounds of exponential moments of simultaneous hitting time for two time-inhomogeneous atomic Markov chains.- Chapter 5. Jin, L., Dimitrov, M, Nim Y.: Valuation and Optimal Strategies for American Options under a Markovian Regime-Switching Model.- Chapter 6. Khusanbaev, Ya.M., Kudratov, Kh.E.: Inequalities for moments of branching processes in a varying environment.- Chapter 7. Kitouni, A., Messaci, F.: A law of the iterated logarithm for the empirical process based upon twice censored data.- Chapter 8. Kolias, P., Papadopoulou, A.: Investigating some attributes of periodicity in DNA sequences via semi-Markov modelling.- Chapter 9. Krasnitskiy, S., Kurchenko, S., Syniavska, O.: Limit Theorems of Baxter Type for Generalized Random Gaussian Processes with Independent Values.- Chapter 10. Lebedev, E., Ponomarov, V., Livinska, H.: On Explicit Formulas of Steady-State Probabilities for the M/M/c/c+m]-Type Retrial Queue.- Chapter 11. Malyarenko, A., Nohrouzian, H.: Testing Cubature Formulae on Wiener Space vs Explicit Pricing Formulae.- Chapter 12. Mishura, Y., Shevchenko, G., Shklyar, S.: Gaussian processes with Volterra kernels.- Chapter 13. Di Nunno, G., Mishura, Y., Ralchenko, K.: Stochastic differential equations driven by additive Volterra--Lévy and Volterra-Gaussian noises.- Chapter 14. Amechi Okeke, G., Abbas, M., Silvestrov, S.: Bochner integrability of the random fixed point of a generalized random operator and almost sure stability of some faster random iterative processes.- Chapter 15. Cruz Rambaud, S.: An Approach to the Absence of Price Bubbles through State-Price Deflators.- Chapter 16. da Silva, J.L., Drumond, C., Streit, L.: Form Factors for Stars Generalized Grey Brownian Motion.- Chapter 17. Silvestrov, D.: Flows of Rare Events for Regularly Perturbed Semi-Markov Processes. Part II. Statistical Methods.- Chapter 18. D’Amico, G., Di Basilio, B., Petroni, F., Gismondi, F.: An econometric analysis of drawdown based measures.- Chapter 19. Anisimov, V., Austin, M.: Forecasting and optimizing patient enrolment in clinical trials under various restrictions.- Chapter 20. Anguzu, C., Engström, C., Kasumba, H., Magero Mango, J.: Algorithms for Recalculating Alpha and Eigenvector Centrality Measures using Graph Partitioning Techniques.- Chapter 21. Kozachenko, Y., Rozora, I.: On statistical properties of the estimator of impulse response function.- Chapter 22. Keikara Muhumuza, A., Malyarenko, A., Silvestrov, S., Mango Magero, J., Kakuba, G.: Connections between the extreme points for Vandermonde determinants and minimizing risk measure in financial mathematics.- Chapter 23. Keikara Muhumuza, A., Malyarenko, A., Lundengard, K., Silvestrov, S., Mango Magero, J., Kakuba, G.: Extreme points of the Vandermonde Determinant and Wishart Ensemble on Symmetric Cones.- Chapter 24. Shchestyuk, N., Tyshenko, S.: Option Pricing and Stochastic Optimization.- Part III. Engineering Mathematics.- Chapter 25. Abela, M.S., Sunil Sharanappa, D.: MHD non-Darcy convective flow and heat transfer over a heated vertical plate embedded in a saturated porous medium in presence of viscous dissipation.- Chapter 26. Arjmand, D.: Numerical upscaling via the wave equation with perfectly matched layers.- Chapter 27. Canpwonyi, S., Carlsson, L.: On the Approximation of Physiologically Structured Population Model with a Three Stage-Structured Population Model in a Grazing System.- Chapter 28. Chandarki, I.M., Singh, B.B.: Homotopy Analysis Method (HAM) for Differential Equations pertaining to the Mixed Convection Boundary-Layer Flow over a Vertical Surface Embedded in a Porous Medium.- Chapter 29. Metri, P.G., Abel, M.S., Sunil Sharanappa, D.: Magnetohydrodynamic Casson nanofluid flow over a Nonlinear Stretching Sheet with Velocity Slip and Convective Boundary Conditions.- Chapter 30. Nankinga, L., Carlsson, L.: A Mathematical Model for Harvesting in a Stage-Structured Cannibalistic System.- Chapter 31. Tawade, J., Metri, P.G.: Mathematical and Computational Analysis of MHD Viscoelastic Fluid Flow and Heat Transfer over Stretching Surface Embedded in a Saturated Porous Medium.- Chapter 32. Tawade, J., Metri, P.G.: Numerical solution of boundary layer flow problem of a Maxwell fluid past a porous stretching surface.- Chapter 33. Umavathi, J.C., Metri, P.G., Silvestrov, S.: Effect of electromagnetic field on mixed convection of two immiscible conducting fluids in a vertical channel.- Chapter 34. Urekar, M.,Djordjević Kozarov, J.: Stochastic Smart Grid Meter for Industry 4.0 - From an Idea to the Practical Prototype.- Chapter 35. Vučković, A., Vučković, D., Perić, M., Raišević, N.: Magnetic force calculation between truncated cone shaped permanent magnet and soft magnetic cylinder using hybrid boundary element method.- Chapter 36. Vujičić, V., Djordjević Kozarov, J., Sovilj, P., Vujičić, B.: Mathematical basis of the stochastic digital measurement method.- Chapter 37. Ögren, M.: Stochastic solutions of Stefan problems.
£208.99
Springer International Publishing AG An Introduction to Anomalous Diffusion and Relaxation
Book SynopsisThis book provides a contemporary treatment of the problems related to anomalous diffusion and anomalous relaxation. It collects and promotes unprecedented applications dealing with diffusion problems and surface effects, adsorption-desorption phenomena, memory effects, reaction-diffusion equations, and relaxation in constrained structures of classical and quantum processes. The topics covered by the book are of current interest and comprehensive range, including concepts in diffusion and stochastic physics, random walks, and elements of fractional calculus. They are accompanied by a detailed exposition of the mathematical techniques intended to serve the reader as a tool to handle modern boundary value problems. This self-contained text can be used as a reference source for graduates and researchers working in applied mathematics, physics of complex systems and fluids, condensed matter physics, statistical physics, chemistry, chemical and electrical engineering, biology, and many others.Table of ContentsPreface.- Integral Transforms and Special Functions.- Concepts in Diffusion and Stochastic Processes.- Random Walks.- Elements of Fractional Calculus.- Fractional Anomalous Diffusion.- Adsorption Phenomena and Anomalous Behavior.- Reaction-Diffusion Problems.- Relaxation under Geometric Constraints I: Classical Processes.- Relaxation under Geometric Constraints II: Quantum Processes.- Index.
£49.49
Springer International Publishing AG An Introduction to Optimal Control Theory: The
Book SynopsisThis book introduces optimal control problems for large families of deterministic and stochastic systems with discrete or continuous time parameter. These families include most of the systems studied in many disciplines, including Economics, Engineering, Operations Research, and Management Science, among many others. The main objective is to give a concise, systematic, and reasonably self contained presentation of some key topics in optimal control theory. To this end, most of the analyses are based on the dynamic programming (DP) technique. This technique is applicable to almost all control problems that appear in theory and applications. They include, for instance, finite and infinite horizon control problems in which the underlying dynamic system follows either a deterministic or stochastic difference or differential equation. In the infinite horizon case, it also uses DP to study undiscounted problems, such as the ergodic or long-run average cost. After a general introduction to control problems, the book covers the topic dividing into four parts with different dynamical systems: control of discrete-time deterministic systems, discrete-time stochastic systems, ordinary differential equations, and finally a general continuous-time MCP with applications for stochastic differential equations. The first and second part should be accessible to undergraduate students with some knowledge of elementary calculus, linear algebra, and some concepts from probability theory (random variables, expectations, and so forth). Whereas the third and fourth part would be appropriate for advanced undergraduates or graduate students who have a working knowledge of mathematical analysis (derivatives, integrals, ...) and stochastic processes.Table of ContentsIntroduction: optimal control problems-. Discrete-time deterministic systems.- Discrete-time stochastic control systems.- Continuous-time deterministic systems.- Continuous-time Markov control processes.- Controlled diffusion processes.- Appendices.- Bibliography.- Index.
£49.49
Springer International Publishing AG Generating Functions in Engineering and the
Book SynopsisGenerating function (GF) is a mathematical technique to concisely represent a known ordered sequence into a simple continuous algebraic function in dummy variable(s). This Second Edition introduces commonly encountered generating functions (GFs) in engineering and applied sciences, such as ordinary GF (OGF), exponential GF (EGF), as also Dirichlet GF (DGF), Lambert GF (LGF), Logarithmic GF (LogGF), Hurwitz GF (HGF), Mittag-Lefler GF (MLGF), etc. This book is intended mainly for beginners in applied science and engineering fields to help them understand single-variable GFs and illustrate how to apply them in various practical problems. Specifically, the book discusses probability GFs (PGF), moment and cumulant GFs (MGF, CGF), mean deviation GFs (MDGF), survival function GFs (SFGF), rising and falling factorial GFs, factorial moment, and inverse factorial moment GFs. Applications of GFs in algebra, analysis of algorithms, bioinformatics, combinatorics, economics, finance, genomics, geometry, graph theory, management, number theory, polymer chemistry, reliability, statistics and structural engineering have been added to this new edition. This book is written in such a way that readers who do not have prior knowledge of the topic can easily follow through the chapters and apply the lessons learned in their respective disciplines.Table of ContentsTypes of Generating Functions.- Operations on Generating Functions.- Generating Functions in Statistics.- Applications of Generating Functions.- Bibliography.
£33.24
Springer International Publishing AG Bioinformatic and Statistical Analysis of Microbiome Data: From Raw Sequences to Advanced Modeling with QIIME 2 and R
Book SynopsisThis unique book addresses the bioinformatic and statistical modelling and also the analysis of microbiome data using cutting-edge QIIME 2 and R software. It covers core analysis topics in both bioinformatics and statistics, which provides a complete workflow for microbiome data analysis: from raw sequencing reads to community analysis and statistical hypothesis testing. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of QIIME 2 and R for data analysis step-by-step. The data as well as QIIME 2 and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter so that these new methods can be readily applied in their own research. Bioinformatic and Statistical Analysis of Microbiome Data is an ideal book for advanced graduate students and researchers in the clinical, biomedical, agricultural, and environmental fields, as well as those studying bioinformatics, statistics, and big data analysis.Table of ContentsChapter 1: Introduction to Linux and Unix(This chapter will introduce some important bioinformatics tools and basics of Linux/Unix system and basic operations with Linux/Unix.) 1.1. Bioinformatics tools and Linux/Unix1.2. Features of Linux/Unix1.3. Interact with Linux/Unix Chapter 2: Introduction to R, RStudio(This chapter will introduce the environment of microbiome data analysis: R, RStudio, and some important R functions and data manipulation skills. All these skills will provide a foundation of bioinformatic and biostatistical analyses of microbiome data.) 2.1. Introduction to R and RStudio2.1.1 Installing R, RStudio, and R Packages2.1.2 Set Working Directory in R2.1.3 Data Analysis through R Studio 2.1.4 Data Import and Export 2.1.5 Basic Data Manipulation2.1. 6 Simple Summary Statistics2.1.7 Other useful R functions2.2. Useful R Packages for Data Management Chapter 3: Bioinformatic Analysis of Next-Generation Sequencing(This chapter will cover next-generation sequencing (NGS) and bioinformatic analysis of NGS data, such as sequencing data quality check, trimming, gene annotation, sequencing alignment, and genome indexing.) 3.1. Introduction to Next-Generation Sequencing3.2. Bioinformatic Analysis of Next-Generation Sequencing3.2.1 Sequencing Data Quality Check3.2.2 Sequencing Data Trimming 3.2.3 Gene Annotation3.2.4 Sequencing Alignment3.2.5 Genome Indexing3.2.6 Remove PCR Duplicates3.3. Introduction to Genome Browsers3.3.1 IGV (Integrative Genome Brower)3.3.2 UCSC Chapter 4: Bioinformatic Analysis of Metagenomics(This chapter will cover bioinformatic analysis of NGS and metagenomics data step by step. The steps will focus on bioinformatic analysis of amplicon sequencing, such as generate OTUs, taxonomic annotation and create OUT table. ) 4.1 Definition of Metagenomics4.2 Amplicon Sequencing4.2.1 Preprocessing4.2.2 Generate OTUs4.2.3 Taxonomic Annotation4.2.4 Create OUT Table4.3 Bioinformatcs Tools for Amplicon Sequencing4.3.1 QIIME 24.3.2 mothur 4.3.3 Bioinformatic Analysis of 16S rRNA Sequence Data using QIIME 2 and mothur4.4 Bioinformatic Analysis of Shortgun Metagenomic Data 4.4.1 Processing of Samples, DNA and Library4.4.2 Quality Checking4.4.3 Assembly4.4.4 Binning4.4.5 Annotation4.4.5.1 Genome and Metagenome Functional Annotations4.4.5.2 Gene Prediction and Functional Annotation Chapter 5: Alpha Diversity(This chapter will introduce biostatistical analysis of alpha diversity of microbiome data. The contents will cover alpha diversity measures and calculations, exploration, statistical hypothesis testing, and power analysis.) 5.1 Introduction to Community Diversities5.1.1 Alpha Diversity5.1.2 Beta Diversity5.2 Alpha Diversity Measures and Calculations5.2.1 Chao 1 Richness Index5.2.2 Shannon-Wiener Diversity Index5.2.3 Simpson Diversity Index5.2.4 Pielou's Evenness Index5.3 Exploration of Alpha Diversity5.3.1 Richness5.3.2 Abundance Bar5.3.3 Heatmap5.3.4 Network 5.3.5 Phylogenetic Tree5.4 Statistical Hypothesis Testing of Alpha Diversity 5.4.1 Two-sample Welch's t-test 5.4.2 Wilcoxon Rank Sum Test 5.4.3 Chi-square Test 5.4.4 One-way ANOVA 5.5.5 Kruskal-Wallis Test 5.5 Multiple Comparisons and Multiple Testing 5.5.1 Pairwise Comparisons 5.5.2 E-value 5.5.3 FWER 5.5.4 FDR5.6. Power Analysis for Testing Differences in Diversity5.6.1 Using power.t.test()5.6.2 Using pwr.avova.test()5.6.3 Using power.prop.test() 5.6.4 Using pwr.chisq.test() 5.6.5 Using power.fisher.test() 5.6.6 Using power.exact.test() Chapter 6: Beta Diversity(This chapter will introduce biostatistical analysis of beta diversity of microbiome data. The contents will cover beta diversity measures and calculations, exploration, ordination, statistical hypothesis testing.) 6.1 Beta Diversity Measures and Calculations6.1.1 Jaccard Index6.1.2 Sørensen Index6.1.3 Bray–Curtis Index6.2 Exploration of Beta Diversity6.2.1 Clustering6.2.1.1 Single Linkage6.2.1.2 Complete Linkage6.2.1.3 Average Linkage 6.2.1.4 Ward’s Minimum Variance6.2.2 Ordination6.2.2.1 Principal Component Analysis (PCA)6.2.2.2 Principal Coordinate Analysis (PCoA) 6.2.2.3 Non-metric multidimensional scaling (NMDS)6.4 Statistical Hypothesis Testing of Beta Diversity 6.4.1 Permutational Multivariate Analysis of Variance (PERMANOVA) 6.4.1.1 Implement PERMANOVA using vegan Package 6.4.1.2 Implement Pairwise Permutational MANOVA using RVAideMemoire Package6.4.2 Analysis of Similarity (ANOSIM) 6.4.2.1 Implement ANOSIM using vegan Package 6.4.3 Compare Microbiome Communities 6.4.3.1 UniFrac, Weighted UniFrac and Generalized UniFrac Distance Metrics 6.4.3.2 Implement Comparison using GUniFrac Package Chapter 7: Differential Abundance Analysis(This chapter will cover two models for count-based differential abundance analysis of microbiome data: negative binomial (NB) models in edgeR and in DESeq2.) 7.1. Count-based Differential Abundance Analysis7.1.1 Biological and Technical Variations7.1.2 Poisson 7.1.3 Negative Binomial (NB)7.2 NB Model in edgeR7.2.1 Exploration of Differential Abundant Taxa7.2.1.1 PCoA7.2.1.2 Heatmap7.2.1.3 Volcano Plot7.2.2 Statistical Hypothesis Testing in edgeR7.2.2.1 The Wald Test7.2.2.2 The Generalized Linear model (GLM)7.3. NB Model in DESeq and DESeq27.3.1 Statistical Hypothesis Testing in DESeq2 7.3.2 Implement DESeq2 Chapter 8: Analyzing Zero-Inflated Microbiome Data(This chapter will introduce both classic and newly developed statistical models for analyzing zero-inflated count microbiome data and show how to use different tests to compare these models. ) 8.1 Zero-inflated Models8.1.1 ZIP Model8.1.2 ZINB Model8.2 Zero-Hurdle Models8.2.1 ZHP Model8.2.2 ZHNB Model8.3 Comparison of Zero-inflated and Zero-Hurdle Models8.3.1 Using Likelihood Ratio Test8.3.2 Using AIC8.3.3 Using BIC8.3.4 Using Vuong Test8.4 Zero-inflated Gaussian (ZIG)8.4.1 Statistical Hypothesis Testing 8.4.1.1 Non-parametric Permutation Test on t-statistics 8.4.1.2 Non-parametric Kruskal-Wallis Test8.4.2 Implement using metagenomeSeq package8.5 Marginalized two-part Beta Regression(MTPBR)8.5.1 Introduction to MTPBR8.4.2 Implement using NLMIXED Procedure8.6 Geometric Mean of Pairwise Ratios (GMPR)8.5.1 Introduction to GMPR8.4.2 Implement using GMPR Package Chapter 9: Compositional Analysis of Microbiome Data(This chapter will summarize the issues of compositional data analysis and introduce the newly developed statistical models and methods for compositional data analysis in microbiome research.) 9.1 Introduction to Compositional Data9.1.1 Aitchison Simplex9.1.2 Fundamental Principles9.1.3 A Family of Log-ratio Transformations 9.1.4 Relative Characteristics of Microbiome Abundance Data9.2 ANOVA-Like Differential Abundance Analysis for Compositional Data9.2.1 Exploratory Compositional Data Analysis9.2.1.1 Compositional Biplot9.2.1. 2 Compositional Scree Plot9.2.1. 3 Compositional Cluster Dendrogram 9.2.1. 4 Compositional Barplot 9.2.2 Using ALDEx2 Package9.3 Analysis of Composition of Microbiomes (ANCOM)9.3.1 Introduction to ANCOM 9.3.2 Implement using ANCOM Package9.4 Balances: a Relative Abundances Perspective for Microbiome Analysis9.4.1 Introduction to Balances9.4.2 Implementing Selection of Balances Using selbal Package Chapter 10: Longitudinal Data Analysis of Microbiome(This chapter will introduce several newly developed statistical models and methods for longitudinal data analysis of microbiome.) 10.1 Zero-inflated Beta Regression Model with Random Effects: ZIBR10.1.1 Statistical Hypothesis Testing of ZIBR10.1.2 Implement using ZIBR Package10.2 Differential Distribution Analysis of Microbiome Data10.1.1 A General Framework of Statistical Hypothesis Testing based on a ZINB10.1.2 Implement using MicrobiomeDDA package10.3 Negative Binomial Mixed Models (NBMMs)10.3.1 Introduction to NBMMs10.3.2 Implement using NBZIMMpackage Chapter 11: Meta-analysis of Microbiome Data (optional)(This chapter will summarize current approaches of meta-analysis of microbiome data and discuss the issues of current approaches. The zero-inflated Beta GAMLSS of meta-analysis of microbiome data will be introduced.) 11.1 Introduction to Meta-analysis in Microbiome Studies11.2 Zero-inflated Beta GAMLSS and Meta-analysis of Microbiome Relative Abundance11.3 Implement using metamicrobiomeR package
£151.99
Springer International Publishing AG A Practical Guide to Atmospheric Simulation
Book SynopsisThis open access title presents atmospheric simulation chambers as effective tools for atmospheric chemistry research. State-of-the-art simulation chambers provide unprecedented opportunities for atmospheric scientists to perform experiments that address the most important questions in air quality and climate research. The book covers technical details about chamber preparation and practical guidelines on their usage, while also delivering relevant historical and contextual information. It not only serves as a key publication for knowledge transfer within the simulation chamber research community, but it also provides the global atmospheric science community with a unique resource that outlines best practice for the operation of simulation chambers. The authors summarize the latest advances in chamber interoperability and standard protocols in order to provide the research community and the next generations of scientists with a unique technical reference guide for the use of simulation chambers. The volume will be of great interest to researchers and graduates working in the fields of Atmospheric and Environmental Sciences.Table of ContentsIntroduction to atmospheric simulation chambers and their applications.- Physical and chemical characterization of the chamber.- Preparation of simulation chambers for experiments.- Preparation of Experiments: Addition and in-situ production of gas phase trace gas and oxidants.- Preparation of the Experiment: Addition of Particles.- Sampling for Offline Analysis.- Analysis of Chamber Data.- Application of simulation chambers to investigate interfacial processes.- Conclusions.
£42.74
Springer International Publishing AG Learning and Intelligent Optimization: 16th
Book SynopsisThis book constitutes the refereed proceedings of the 16th International Conference on Learning and Intelligent Optimization, LION 16, which took place in Milos Island, Greece, in June 2022.The 36 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 60 submissions. LION deals with automatic solver configuration, parallel methods, intelligent optimization, nature-inspired algorithms, hard combinatorial optimization problems, DC learning, computational intelligence, and others. The contributions were organized in topical sections as follows: Invited Papers; Contributed Papers.Table of ContentsInvited Papers.- Optimal Scheduling of the Leaves of a Tree and the SVO Frequencies of Languages.- From Design of Experiments to Combinatorics of Disasters: A Conceptual Framework for Disaster Exercises.- Separating two polyhedra utilizing alternative theorems and penalty function.- Contributed Papers. -A Composite Index Method for Optimization Benchmarking.- Optimal Energy Management of Microgrid Using Multi-objective Optimisation Approach.- A Stochastic Alternating Balance k-Means Algorithm for Fair Clustering.- Binary Black Widow Optimization Algorithm for Feature Selection Problems.- Learning to Solve a Stochastic Orienteering Problem with Time Windows.- ML-based approach for accelerating global search algorithm for solving multicriteria problems .- The Skewed Kruskal algorithm.- Bounds for sparse solutions of K-SVCR multi-class classification model.- Integer Linear Programming in Solving an Optimization Problem at the Mixing Department of the Metallurgical Production.- Realtime Gray-Box Algorithm Configuration.- Dynamic urban solid waste management system for smart cities.- Single MCMC Chain Parallelisation on Decision Trees.- Single MCMC Chain Parallelisation on Decision Trees.- Competitive supply allocation in a distribution network under overproduction.- Safe-exploration of control policies from safe-experience via Gaussian Processes.- Bayesian Optimization in Wasserstein Spaces.- Network Vulnerability Analysis in Wasserstein Spaces.- BERT Self-Learning Approach with Limited Labels for Document Classification.- Autonomous Learning Optimization for Deep Learning.- Optimizing Data Augmentation Policy through Random Unidimensional Search.- Evaluating Student Behaviour on the MathE Platform - Clustering Algorithms Approaches.- Unsupervised Training for Neural TSP Solver.- Comparing surrogate models for tuning optimization algorithms.- Search and Score-based Waterfall Auction Optimization.- Survey on KNN Methods in Data Science.- Constrained Shortest Path and Hierarchical Structures.- Investigation of Graph Neural Networks for Instance Segmentation of Industrial Point Cloud Data.- Fitness landscape ruggedness impact on PSO in dealing with three variants of the travelling salesman problem.- A Multi-UAVs’ Provider Model for the provision of 5G Service Chains: a game theoretic approach.- Metabolic Syndrome Risk Forecasting on Elderly with ML Techniques.- Airport Digital Twins for Resilient Disaster Management Response.- Strategies for Surviving Aggressive Multiparty Repeated Standoffs.- A Hybridization of GRASP and UTASTAR for Solving the Vehicle Routing Problem with Pickups and Deliveries and 3D Loading Constraints.- Packing hypertrees and the k-cut problem in Hypergraphs.- Maximizing the Eigenvalue-Gap and Promoting Sparsity of Doubly Stochastic Matrices with PSO.- Value of Information in the Mean-Square Case and its Application to the Analysis of Financial Time-Series Forecast.
£66.49
Springer International Publishing AG Mathematical Methods for Engineering and Science
Book Synopsis< div="">This book introduces undergraduate students of engineering and science to applied mathematics essential to the study of many problems. Topics are differential equations, power series, Laplace transforms, matrices and determinants, vector analysis, partial differential equations, complex variables, and numerical methods. Approximately, 160 examples and 1000 homework problems aid students in their study. This book presents mathematical topics using derivations rather than theorems and proofs. This textbook is uniquely qualified to apply mathematics to physical applications (spring-mass systems, electrical circuits, conduction, diffusion, etc.), in a manner that is efficient and understandable. This book is written to support a mathematics course after differential equations, to permit several topics to be covered in one semester, and to make the material comprehensible to undergraduates. An Instructor Solutions Manual, and also a Student Solutions Manual that provides solutions to select problems, is available.^Table of ContentsDocument Attached
£75.99
Springer International Publishing AG RealWorld Evidence in Medical Product Development
Book SynopsisThis book provides state-of-art statistical methodologies, practical considerations from regulators and sponsors, logistics, and real use cases for practitioners for the uptake of RWE/D.
£98.99
Birkhauser Verlag AG Industrial Statistics
Book SynopsisThis innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource.
£71.24
Springer International Publishing AG Applied Calculus with R
Book SynopsisThis textbook integrates scientific programming with the use of R and uses it both as a tool for applied problems and to aid in learning calculus ideas. Adding R, which is free and used widely outside academia, introduces students to programming and expands the types of problems students can engage. There are no expectations that a student has any coding experience to use this text. While this is an applied calculus text including real world data sets, a student that decides to go on in mathematics should develop sufficient algebraic skills so that they can be successful in a more traditional second semester calculus course. Hopefully, the applications provide some motivation to learn techniques and theory and to take additional math courses. The book contains chapters in the appendix for algebra review as algebra skills can always be improved. Exercise sets and projects are included throughout with numerous exercises based on graphs.
£49.49
Springer International Publishing AG Foundations of Modern Statistics: Festschrift in
Book SynopsisThis book contains contributions from the participants of the international conference “Foundations of Modern Statistics” which took place at Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, during November 6–8, 2019, and at Higher School of Economics (HSE University), Moscow, during November 30, 2019. The events were organized in honor of Professor Vladimir Spokoiny on the occasion of his 60th birthday. Vladimir Spokoiny has pioneered the field of adaptive statistical inference and contributed to a variety of its applications. His more than 30 years of research in the field of mathematical statistics had a great influence on the development of the mathematical theory of statistics to its present state. It has inspired many young researchers to start their research in this exciting field of mathematics. The papers contained in this book reflect the broad field of interests of Vladimir Spokoiny: optimal rates and non-asymptotic bounds in nonparametrics, Bayes approaches from a frequentist point of view, optimization, signal processing, and statistical theory motivated by models in applied fields. Materials prepared by famous scientists contain original scientific results, which makes the publication valuable for researchers working in these fields. The book concludes by a conversation of Vladimir Spokoiny with Markus Reiβ and Enno Mammen. This interview gives some background on the life of Vladimir Spokoiny and his many scientific interests and motivations. Table of ContentsOptimal rates and non-asymptotic bounds in nonparametrics: Z. Harchaoui, A. Juditsky, A. Nemirovski, D. Ostrovskii, Adaptive Denoising of Signals with Local Shift-Invariant Structure.- A. Dubois, Thomas B. Berret, C. Butucea, Goodness-of-fit testing for Hölder continuous densities under local differential privacy.- G. Blanchard and J.ean-Baptiste Fermanian, Nonasymptotic signal detection and two-sample tests in high dimension.- Sara van de Geer and P. Hinz, The Lasso with structured design and entropy of (absolute) convex hulls.- M. Hiabu, E. Mammen and Joseph-Theo Meyer, Local linear smoothing in additive models as data projection.- S. Ayvazyan and V. Ulyanov, A multivariate CLT for „typical“ weighted sums with rate of convergence of order O(1/n).- Estimation of matrices and subspaces: F. Götze, A. Tikhomirov, D. Timushev, Rate of convergence for sample covariance sparse matrices.- M. Wahl, Van Trees inequality, group equivariance, and estimation of principal subspaces.- D. Belomestny, E. Krymova, Sparse constrained projection approximation subspace tracking Nonparametric and semiparametric Bayes statistics.- Natalia Bochkina: Bernstein - von Mises theorem and misspecified models: a review.- M. Panov, On accuracy of Gaussian approximation in Bayesian semiparametric problems.- Statistical theory motivated by applications: M. Bl ́ehaut, X. D’Haultfœuille, J ́er ́emy L’Hour, A. B. Tsybakov, An alternative to synthetic control for models with many covariates under sparsity.- C. Breunig, X. Chen, Adaptive Estimation of Quadratic Functionals in Nonparametric Instrumental Variable Models.- G. Kulaitis, A. Munk and F. Werner, A minimax testing perspective on spatial statistical resolution in microscopy.- Optimisation: P. Dvurechensky, A. Gasnikov, A. Tyurin and V. Zholobov, Unifying Framework for Accelerated Randomized Methods in Convex Optimization.- K. Khowaja, M. Shcherbatyy and W. Karl Härdle. Surrogate Models for Optimization of Dynamical Systems.- Interview with Vladimir Spokoiny.
£142.49
Springer International Publishing AG Concentration and Gaussian Approximation for
Book SynopsisThis book describes extensions of Sudakov's classical result on the concentration of measure phenomenon for weighted sums of dependent random variables. The central topics of the book are weighted sums of random variables and the concentration of their distributions around Gaussian laws. The analysis takes place within the broader context of concentration of measure for functions on high-dimensional spheres. Starting from the usual concentration of Lipschitz functions around their limiting mean, the authors proceed to derive concentration around limiting affine or polynomial functions, aiming towards a theory of higher order concentration based on functional inequalities of log-Sobolev and Poincaré type. These results make it possible to derive concentration of higher order for weighted sums of classes of dependent variables.While the first part of the book discusses the basic notions and results from probability and analysis which are needed for the remainder of the book, the latter parts provide a thorough exposition of concentration, analysis on the sphere, higher order normal approximation and classes of weighted sums of dependent random variables with and without symmetries. Table of ContentsPart I. Generalities.- 1. Moments and correlation conditions.- 2. Some classes of probability distributions.- 3. Characteristic functions.- 4. Sums of independent random variables.- Part II. Selected topics on concentration.- 5. Standard analytic conditions.- 6. Poincaré-type inequalities.- 7. Logarithmic Sobolev inequalities.- 8. Supremum and infimum convolutions.- Part IV. Analysis on the sphere.- 9. Sobolev-type inequalities.- 10. Second order spherical concentration.- 11. Linear functionals on the sphere.- Part V. First applications to randomized sums.- 12. Typical distributions.- 13. Characteristic functions of weighted sums.- 14. Fluctuations of distributions.- Part VI. Refined bounds and rates.- 15. L^2 expansions and estimates.- 16. Refinements for the Kolmogorov distance.- 17. Applications of the second order correlation condition.- Part VII. Distributions and coefficients of special types.- 18. Special systems and examples.- 19. Distributions with symmetries.- 20. Product measures.- 21. Coefficients of Special type.- Glossary.
£113.99
Springer International Publishing AG Learning for Decision and Control in Stochastic
Book SynopsisThis book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research. The authors present the topic based on a general stochastic network optimization model, and review several important theoretical tools that are widely adopted in network research, including convex optimization, the drift method, and mean-field analysis. The book then covers several popular learning-based methods, i.e., learning-augmented drift, multi-armed bandit and reinforcement learning, along with applications in networks where the techniques have been successfully applied. The authors also provide a discussion on potential future directions and challenges.Table of ContentsIntroduction.- The Stochastic Network Model.- Network Optimization Techniques.- Learning Network Decisions.- Summary and Discussions.
£42.74
Springer International Publishing AG Learning for Decision and Control in Stochastic
Book SynopsisThis book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research.
£42.74
Springer International Publishing AG Descriptive Statistics for Scientists and
Book Synopsis
£37.99
Springer International Publishing AG Generalized Linear Mixed Models with Applications
Book SynopsisThis open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables. Table of ContentsChapter 1) Elements of the Generalized Linear Mixed Models.- Chapter 2) Generalized Linear Models.- Chapter 3) Objectives in Model Inference.- Chapter 4) Generalized Linear Mixed Models for non-normal responses.- Chapter 5) Generalized Linear Mixed Models for Count response.- Chapter 6) Generalized Linear Mixed Models for Proportions and Percentages response.- Chapter 7) Times of occurrence of an event of interest.- Chapter 8) Generalized Linear Mixed Models for Categorial and Ordinal responses.- Chapter 9) Generalized Linear Mixed Models for Repeated Measurements.
£33.24
Springer International Publishing AG Continuous Parameter Markov Processes and
Book SynopsisThis graduate text presents the elegant and profound theory of continuous parameter Markov processes and many of its applications. The authors focus on developing context and intuition before formalizing the theory of each topic, illustrated with examples.After a review of some background material, the reader is introduced to semigroup theory, including the Hille–Yosida Theorem, used to construct continuous parameter Markov processes. Illustrated with examples, it is a cornerstone of Feller’s seminal theory of the most general one-dimensional diffusions studied in a later chapter. This is followed by two chapters with probabilistic constructions of jump Markov processes, and processes with independent increments, or Lévy processes. The greater part of the book is devoted to Itô’s fascinating theory of stochastic differential equations, and to the study of asymptotic properties of diffusions in all dimensions, such as explosion, transience, recurrence, existence of steady states, and the speed of convergence to equilibrium. A broadly applicable functional central limit theorem for ergodic Markov processes is presented with important examples. Intimate connections between diffusions and linear second order elliptic and parabolic partial differential equations are laid out in two chapters, and are used for computational purposes. Among Special Topics chapters, two study anomalous diffusions: one on skew Brownian motion, and the other on an intriguing multi-phase homogenization of solute transport in porous media.Table of Contents1. A review of Martingaels, stopping times and the Markov property.- 2. Semigroup theory and Markov processes.-3. Regularity of Markov process sample paths.- 4. Continuous parameter jump Markov processes.- 5. Processes with independent increments.- 6. The stochastic integral.- 7. Construction of difficusions as solutions of stochastic differential equations.- 8. Itô's Lemma.- 9. Cameron-Martin-Girsanov theorem.- 10. Support of nonsingular diffusions.- 11. Transience and recurrence of multidimensional diffusions.- 12. Criteria for explosion.- 13. Absorption, reflection and other transformations of Markov processes.- 14. The speed of convergence to equilibrium of discrete parameter Markov processes and Diffusions.- 15. Probabilistic representation of solutions to certain PDEs.- 16. Probabilistic solution of the classical Dirichlet problem.- 17. The functional Central Limit Theorem for ergodic Markov processes.- 18. Asymptotic stability for singular diffusions.- 19. Stochastic integrals with L2-Martingales.- 20. Local time for Brownian motion.- 21. Construction of one dimensional diffusions by Semigroups.- 22. Eigenfunction expansions of transition probabilities for one-dimensional diffusions.- 23. Special Topic: The Martingale Problem.- 24. Special topic: multiphase homogenization for transport in periodic media.- 25. Special topic: skew random walk and skew Brownian motion.- 26. Special topic: piecewise deterministic Markov processes in population biology.- A. The Hille-Yosida theorem and closed graph theorem.- References.- Related textbooks and monographs.
£79.99
Birkhauser Verlag AG Discrete-Time Semi-Markov Random Evolutions and
Book SynopsisThis book extends the theory and applications of random evolutions to semi-Markov random media in discrete time, essentially focusing on semi-Markov chains as switching or driving processes. After giving the definitions of discrete-time semi-Markov chains and random evolutions, it presents the asymptotic theory in a functional setting, including weak convergence results in the series scheme, and their extensions in some additional directions, including reduced random media, controlled processes, and optimal stopping. Finally, applications of discrete-time semi-Markov random evolutions in epidemiology and financial mathematics are discussed. This book will be of interest to researchers and graduate students in applied mathematics and statistics, and other disciplines, including engineering, epidemiology, finance and economics, who are concerned with stochastic models of systems.Table of Contents- 1. Discrete-Time Stochastic Calculus in Banach Space. - 2. Discrete-Time Semi-Markov Chains. - 3. Discrete-Time Semi-Markov Random Evolutions. - 4. Weak Convergence of DTSMRE in Series Scheme. - 5. DTSMRE in Reduced Random Media. - 6. Controlled Discrete-Time Semi-Markov Random Evolutions. - 7. Epidemic Models in Random Media. - 8. Optimal Stopping of Geometric Markov Renewal Chains and Pricing.
£98.99
Springer International Publishing AG Non-Academic Careers for Quantitative Social
Book SynopsisThis book is a guide to non-academic careers for quantitative social scientists. Written by social science PhDs working in large corporations, non-profits, tech startups, and alt-academic positions in higher education, this book consists of more than a dozen chapters on various topics on finding rewarding careers outside the academy. Chapters are organized in three parts. Part I provides an introduction to the types of jobs available to social science PhDs, where those jobs can be found, and what the work looks like in those positions. Part II creates a guide for social science PhDs on how to set themselves up for such careers, including navigating the academic world of graduate school while contemplating non-academic options, and selling their academic experience in a non-academic setting. Part III offers perspectives on timelines for making non-academic career decisions, lifestyle differences between academia and non-academic jobs, and additional resources for those considering a non-academic route. Providing valuable insight on non-academic careers from those who have successfully made the transition, this volume will be an asset to graduate students, advisors, and recent PhDs, in quantitative social science. Table of ContentsChapter 1. Introduction: Surveying the Landscape of Industry Jobs.- Section 1: Career Paths.- Chapter 2. Data Science Needs You, Social Scientist.- Chapter 3. How to Thrive in the Data Industry Without a Traditional STEM Background.- Chapter 4. Alt-Academic Career Paths.- Chapter 5. From the Academy to Tech Startups: Considerations and Opportunities.- Chapter 6. Opportunities and Pathways in Survey Research.- Chapter 7. Market Research with a PhD in Sociology.- Chapter 8. Say Yes to Cultivating Your Future.- Chapter 9. Working in Government.- Chapter 10. Working in Quasi-Governmental Research.- Chapter 11. Proudly Disinterested: Public Administration and Social Science Ph.D. Programs.- Chapter 12. Applying the Transferrable Skill Set of a Ph.D. to Emerging Data Fieldsx.- Section 2: Advice for Non-Academic Job Success.- Chapter 13. How to Market Yourself for Careers Beyond the Professoriate.- Chapter 14. Beyond Visa Sponsorship: Navigating the Job Market as An Immigrant.- Chapter 15. So You Want to Work in Tech. How Do You Make the Leap?.- Chapter 16. Perspectives on Rapid Antigen Tests for Downstream Validation and Development of Theranostics.- Chapter 17. Kill, Pivot, Continue: Tips and Tricks for Career Transition Away From Academe.- Chapter 18. Presenting Academic Research in the Interview Process and Beyond: A Conversation Between Colleagues.- Chapter 19. Thriving in a Non-Academic Environment.- Chapter 20. You Got Your First Job, What About Your Second? Conversations with Women Social Scientists on Landing Multiple Non-Academic Jobs.- Chapter 21. Staying Academically Relevant in a Non-Academic Career.-
£71.24
Springer International Publishing AG Optimal Experimental Design: A Concise
Book SynopsisThis textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area. It presents the common concepts and techniques for linear and nonlinear models as well as Bayesian optimal designs. The last two chapters are devoted to particular themes of interest, including recent developments and hot topics in optimal experimental design, and real-world applications. Numerous examples and exercises are included, some of them with solutions or hints, as well as references to the existing software for computing designs. The book is primarily intended for graduate students and young researchers in statistics and applied mathematics who are new to the field of optimal experimental design. Given the applications and the way concepts and results are introduced, parts of the text will also appeal to engineers and other applied researchers.Table of ContentsPreface.- Motivating Introduction.- Linear Models.- Nonlinear Models.- Bayesian Optimal Designs.- Hot Topics.- Real Case Examples.- Appendices.- References.- Index.
£59.99
Springer International Publishing AG Peeling Random Planar Maps: École d’Été de
Book SynopsisThese Lecture Notes provide an introduction to the study of those discrete surfaces which are obtained by randomly gluing polygons along their sides in a plane. The focus is on the geometry of such random planar maps (diameter, volume growth, scaling and local limits...) as well as the behavior of statistical mechanics models on them (percolation, simple random walks, self-avoiding random walks...).A “Markovian” approach is adopted to explore these random discrete surfaces, which is then related to the analogous one-dimensional random walk processes. This technique, known as "peeling exploration" in the literature, can be seen as a generalization of the well-known coding processes for random trees (e.g. breadth first or depth first search). It is revealed that different types of Markovian explorations can yield different types of information about a surface. Based on an École d'Été de Probabilités de Saint-Flour course delivered by the author in 2019, the book is aimed at PhD students and researchers interested in graph theory, combinatorial probability and geometry. Featuring open problems and a wealth of interesting figures, it is the first book to be published on the theory of random planar maps.Table of Contents- Part I (Planar) Maps. - 1. Discrete Random Surfaces in High Genus. - 2. Why Are Planar Maps Exceptional?. - 3. The Miraculous Enumeration of Bipartite Maps. - Part II Peeling Explorations. - 4. Peeling of Finite Boltzmann Maps. - 5. Classification of Weight Sequences. - Part III Infinite Boltzmann Maps. - 6. Infinite Boltzmann Maps of the Half-Plane. - 7. Infinite Boltzmann Maps of the Plane. - 8. Hyperbolic Random Maps. - 9. Simple Boundary, Yet a Bit More Complicated. - 10. Scaling Limit for the Peeling Process. - Part IV Percolation(s). - 11. Percolation Thresholds in the Half-Plane. - 12. More on Bond Percolation. - Part V Geometry. - 13. Metric Growths. - 14. A Taste of Scaling Limit. - Part VI Simple Random Walk. - 15. Recurrence, Transience, Liouville and Speed. - 16. Subdiffusivity and Pioneer Points.
£49.49
Springer International Publishing AG Time Series Econometrics: Learning Through
Book SynopsisRevised and updated for the second edition, this textbook allows students to work through classic texts in economics and finance, using the original data and replicating their results. In this book, the author rejects the theorem-proof approach as much as possible, and emphasizes the practical application of econometrics. They show with examples how to calculate and interpret the numerical results.This book begins with students estimating simple univariate models, in a step by step fashion, using the popular Stata software system. Students then test for stationarity, while replicating the actual results from hugely influential papers such as those by Granger & Newbold, and Nelson & Plosser. Readers will learn about structural breaks by replicating papers by Perron, and Zivot & Andrews. They then turn to models of conditional volatility, replicating papers by Bollerslev. Students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger. Finally, students estimate static and dynamic panel data models, replicating papers by Thompson, and Arellano & Bond.The book contains many worked-out examples, and many data-driven exercises. While intended primarily for graduate students and advanced undergraduates, practitioners will also find the book useful.“How to best start learning time series econometrics? Learning by doing. This is the ethos of this book. What makes this book useful is that it provides numerous worked out examples along with basic concepts. It is a fresh, no-nonsense, practical approach that students will love when they start learning time series econometrics. I recommend this book strongly as a study guide for students who look for hands-on learning experience."--Professor Sokbae "Simon" Lee, Columbia University, Co-Editor of Econometric Theory and Associate Editor of Econometrics Journal. Table of ContentsIntroduction.- ARMA(p,q) Processes.- Model Selection in ARMA(p,q) processes.- Stationarity and Invertibility.- Non-stationarity and ARIMA(p,d,q) processes.- Seasonal ARMA(p,q) processe.- Unit root tests.- Structural Breaks.- ARCH, GARCH and Time-varying Variance.- Vector Autoregressions I: Basics.- Vector Autoregressions II: Extensions.- Cointegration and VECMs.- Static Panel Data Models.- Dynamic Panel Data Models.- Conclusion.
£80.99
Springer International Publishing AG Probability: An Introduction Through Theory and
Book SynopsisThis textbook offers a complete one-semester course in probability, covering the essential topics necessary for further study in the areas of probability and statistics. The book begins with a review of the fundamentals of measure theory and integration. Probability measures, random variables, and their laws are introduced next, along with the main analytic tools for their investigation, accompanied by some applications to statistics. Questions of convergence lead to classical results such as the law of large numbers and the central limit theorem with their applications also to statistical analysis and more. Conditioning is the next main topic, followed by a thorough introduction to discrete time martingales. Some attention is given to computer simulation. Through the text, over 150 exercises with full solutions not only reinforce the concepts presented, but also provide students with opportunities to develop their problem-solving skills, and make this textbook suitable for guided self-study. Based on years of teaching experience, the author's expertise will be evident in the clear presentation of material and the carefully chosen exercises. Assuming familiarity with measure and integration theory as well as elementary notions of probability, the book is specifically designed for teaching in parallel with a first course in measure theory. An invaluable resource for both instructors and students alike, it offers ideal preparation for further courses in statistics or probability, such as stochastic calculus, as covered in the author's book on the topic.Table of Contents1 Elements of Measure Theory.- 2 Probability.- 3 Convergence.- 4 Conditioning.- 5 Martingales.- 6 Complements.- 7 Solutions.
£999.99
Springer International Publishing AG An Introduction to Statistical Learning: with
Book SynopsisAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.Table of ContentsIntroduction.- Statistical Learning.- Linear Regression.- Classification.- Resampling Methods.- Linear Model Selection and Regularization.- Moving Beyond Linearity.- Tree-Based Methods.- Support Vector Machines.- Deep Learning.- Survival Analysis and Censored data.- Unsupervised Learning.- Multiple Testing.- Index.
£85.49
Springer International Publishing AG Probability and Statistics for STEM
Book SynopsisThis new edition presents the essential topics in probability and statistics from a rigorous standpoint. After providing an overview of the basics of probability, the authors cover essential topics such as confidence intervals, hypothesis testing, and linear regression.
£999.99
Springer An Introduction to Statistical Learning: With
Book SynopsisIntroduction.- Statistical Learning.- Linear Regression.- Classification.- Resampling Methods.- Linear Model Selection and Regularization.- Moving Beyond Linearity.- Tree-Based Methods.- Support Vector Machines.- Deep Learning.- Survival Analysis and Censored data.- Unsupervised Learning.- Multiple Testing.- Index.
£62.99
Springer International Publishing AG Potential Functions of Random Walks in ℤ with
Book SynopsisThis book studies the potential functions of one-dimensional recurrent random walks on the lattice of integers with step distribution of infinite variance. The central focus is on obtaining reasonably nice estimates of the potential function. These estimates are then applied to various situations, yielding precise asymptotic results on, among other things, hitting probabilities of finite sets, overshoot distributions, Green functions on long finite intervals and the half-line, and absorption probabilities of two-sided exit problems.The potential function of a random walk is a central object in fluctuation theory. If the variance of the step distribution is finite, the potential function has a simple asymptotic form, which enables the theory of recurrent random walks to be described in a unified way with rather explicit formulae. On the other hand, if the variance is infinite, the potential function behaves in a wide range of ways depending on the step distribution, which the asymptotic behaviour of many functionals of the random walk closely reflects.In the case when the step distribution is attracted to a strictly stable law, aspects of the random walk have been intensively studied and remarkable results have been established by many authors. However, these results generally do not involve the potential function, and important questions still need to be answered. In the case where the random walk is relatively stable, or if one tail of the step distribution is negligible in comparison to the other on average, there has been much less work. Some of these unsettled problems have scarcely been addressed in the last half-century. As revealed in this treatise, the potential function often turns out to play a significant role in their resolution. Aimed at advanced graduate students specialising in probability theory, this book will also be of interest to researchers and engineers working with random walks and stochastic systems. Table of ContentsPreface.- Introduction.- Preliminaries.- Bounds of the Potential Function.- Some Explicit Asymptotic Forms of a(x).- Applications Under m+/m → 0.- The Two-Sided Exit Problem – General Case.- The Two-Sided Exit Problem for Relatively Stable Walks.- Absorption Problems for Asymptotically Stable Random Walks.- Asymptotically Stable RandomWalks Killed Upon Hitting a Finite Set.- Appendix.- References.- Notation Index.- Subject Index.
£49.49
Springer International Publishing AG Statistical Methods: Connections, Equivalencies,
Book SynopsisThe primary purpose of this book is to introduce the reader to a wide variety of interesting and useful connections, relationships, and equivalencies between and among conventional and permutation statistical methods. There are approximately 320 statistical connections and relationships described in this book. For each connection or connections the tests are described, the connection is explained, and an example analysis illustrates both the tests and the connection(s). The emphasis is more on demonstrations than on proofs, so little mathematical expertise is assumed. While the book is intended as a stand-alone monograph, it can also be used as a supplement to a standard textbook such as might be used in a second- or third-term course in conventional statistical methods. Students, faculty, and researchers in the social, natural, or hard sciences will find an interesting collection of statistical connections and relationships - some well-known, some more obscure, and some presented here for the first time.Table of Contents- 1. Introduction. - 2. Statistical Methods. - 3. One-Sample Tests. - 4. Two-Sample Tests. - 5. Matched-Pair Tests. - 6. Completely Randomized Designs. - 7. Randomized-Blocks Designs. - 8. Measures of Interval Association. - 9. Measures of Ordinal Association I. - 10. Measures of Ordinal Association II. - 11. Measures of Nominal Association I. - 12. Measures of Nominal Association II. - 13. Measures of Fourfold Association I. - 14. Measures of Fourfold Association II.
£132.99
Springer International Publishing AG Analysis of Epidemiologic Data Using R
Book SynopsisThis book addresses the description and analysis of occurrence data frequently encountered in epidemiological studies. With the occurrence of Covid-19, people have been exposed to the analysis and interpretation of epidemiological data. To be informed consumers of this information, people need to understand the nature and analysis of these data. Effort is made to emphasize concepts rather than mathematics. Subjects range from description of the frequencies of disease to the analysis of associations between the occurrence of disease and exposure. Those analyses begin with simple associations and work up to complex relationships that involve the control of extraneous characteristics. Analyses rely on the statistical software R, which is freeware in wide use by professional epidemiologists and other scientists.Table of ContentsIntroduction to R.- Measures of Disease Frequency.- Measures of Association.- 2x2 Tables.- Life Tables.- Control of Confounding.
£33.24
Springer TrotterKato Approximations of Stochastic
Book Synopsis1 Introduction and Motivating Examples.- 2 Mathematical Machinery.- 3 Trotter-Kato Approximations of Stochastic Differential Equations.- 4 Trotter-Kato Approximations of Stochastic Differential Equations in UMD Banach Spaces.- 6 Applications to Stochastic Optimal Control.- Appendix A: Nuclear and Hilbert-Schmidt Operators.- Appendix B: Convergence of Analytic Semigroups.- Appendix C: The Pettis Measurability Theorem.- Appendix D: R-Boundedness and ?-Boundedness.- Appendix E: The Feynman-Kac Formula.- Bibliographical Notes and Remarks.- Bibliography.
£98.99
Springer International Publishing AG Practical Applications of Stochastic Modelling:
Book SynopsisThis book constitutes the referred proceedings of the 11th International Workshop on Practical Applications of Stochastic Modelling, PASM 2022, was held in Alicante, Spain, in September 2022.The 7 full papers presented in this volume were carefully reviewed and selected from 9 submissions. The papers demonstrate a diverse set of applications and approaches of stochastic modelling.Table of ContentsPerformance modelling of attack graphs.- Towards Calculating the Resilience of a Urban Transport Network under Attack.- Analysis of the Battery Level in Complex Wireless Sensor Networks using a Two Time Scales Second Order Fluid Model.- To Confine or not to Confine: A Mean Field Game Analysis of the End of an Epidemic.- Data Center Organization and Optimization Strategy as a k-ary n-cube Topology.- Towards energy-aware management of shared printers.- Modelling Performance and Fairness of Frame Bursting in IEEE 802.11n using PEPA.
£49.49
Springer International Publishing AG Marginal and Functional Quantization of
Book SynopsisVector Quantization, a pioneering discretization method based on nearest neighbor search, emerged in the 1950s primarily in signal processing, electrical engineering, and information theory. Later in the 1960s, it evolved into an automatic classification technique for generating prototypes of extensive datasets. In modern terms, it can be recognized as a seminal contribution to unsupervised learning through the k-means clustering algorithm in data science. In contrast, Functional Quantization, a more recent area of study dating back to the early 2000s, focuses on the quantization of continuous-time stochastic processes viewed as random vectors in Banach function spaces. This book distinguishes itself by delving into the quantization of random vectors with values in a Banach space—a unique feature of its content. Its main objectives are twofold: first, to offer a comprehensive and cohesive overview of the latest developments as well as several new results in optimal quantization theory, spanning both finite and infinite dimensions, building upon the advancements detailed in Graf and Luschgy's Lecture Notes volume. Secondly, it serves to demonstrate how optimal quantization can be employed as a space discretization method within probability theory and numerical probability, particularly in fields like quantitative finance. The main applications to numerical probability are the controlled approximation of regular and conditional expectations by quantization-based cubature formulas, with applications to time-space discretization of Markov processes, typically Brownian diffusions, by quantization trees. While primarily catering to mathematicians specializing in probability theory and numerical probability, this monograph also holds relevance for data scientists, electrical engineers involved in data transmission, and professionals in economics and logistics who are intrigued by optimal allocation problems.Table of ContentsPreface.- Notation Index.- Part I. Basics and Marginal Quantization.- 1. Optimal and Stationary Quantizers.- 2. The Finite-Dimensional Setting I.- 3. The Finite-Dimensional Setting II.- Part II. Functional Quantization.- 4. Functional Quantization, Small Ball Probabilities, Metric Entropy and Series Expansions for Gaussian Processes.- 5. Spectral Methods for Gaussian Processes.- 6. Geometry of Optimal and Rate-Optimal Quantizers for Gaussian Processes.- 7. Mean Regular Processes.- Part III. Algorithmic Aspects and Applications:- 8. Optimal Quantization from the Numerical Side (Static).- 9. Applications: Quantization-Based Cubature Formulas.- 10. Quantization-Based Numerical Schemes.- Appendices.- A. Radon Random Vectors, Stochastic Processes and Inequalities.- B. Miscellany.- References.- Index.
£161.99
Springer International Publishing AG Applied Statistics for Business and Management
Book SynopsisThis book illustrates the capabilities of Microsoft Excel to teach applied statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical statistical problems in industry. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in statistics courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past.The 2nd edition of Applied Business Statistics for Business and Management capitalizes on these improvements by teaching students and practitioners how to apply Excel to statistical techniques necessary in their courses and workplace. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand business problems. Practice problems are provided at the end of each chapter with their solutions.Table of ContentsStatistics and Data.- Summarizing Data.- Descriptive Statistics and Graphing.- Normal World.- Survey Design.- Sampling.- Inference.- Probability.- Correlation.- Simple Linear Regression.- Multiple Regression.- Significance Tests.- Non Linear Regression.- Survey Reports.
£94.99
Springer International Publishing AG Proceedings of the 6th International Symposium on
Book SynopsisThis proceedings book covers a wide range of topics related to uncertainty analysis and its application in various fields of engineering and science. It explores uncertainties in numerical simulations for soil liquefaction potential, the toughness properties of construction materials, experimental tests on cyclic liquefaction potential, and the estimation of geotechnical engineering properties for aerogenerator foundation design. Additionally, the book delves into uncertainties in concrete compressive strength, bio-inspired shape optimization using isogeometric analysis, stochastic damping in rotordynamics, and the hygro-thermal properties of raw earth building materials. It also addresses dynamic analysis with uncertainties in structural parameters, reliability-based design optimization of steel frames, and calibration methods for models with dependent parameters. The book further explores mechanical property characterization in 3D printing, stochastic analysis in computational simulations, probability distribution in branching processes, data assimilation in ocean circulation modeling, uncertainty quantification in climate prediction, and applications of uncertainty quantification in decision problems and disaster management. This comprehensive collection provides insights into the challenges and solutions related to uncertainty in various scientific and engineering contexts.Table of ContentsUncertainties of numerical simulation for static liquefaction potential of saturated soils.- Uncertainties about the Toughness Property of raw earth construction materials.- Uncertainties of Experimental Tests on Cyclic Liquefaction Potential of Unsaturated Soils.- Analysis of the Impact of Uncertainties on the Estimation of Geotechnical Engineering Properties of Soil from SPT on the Design of Aerogenerators Foundation.- Uncertainties on the unconfined compressive strength of raw and textured concrete.- Bio-inspired shape optimization for structural robust design using isogeometric analysis.
£189.99
£179.99
Springer International Publishing Multimodal and Tensor Data Analytics for
Book Synopsis
£104.49
Springer Nature Switzerland A Cookbook with Probability One
Book SynopsisThe fundamental concepts of random variable/vector and probability distributions are introduced beforehand with respect to the usual treatment of this subject in standard probability textbook, trying to strike a balance between precise mathematical definitions and their applied knowledge.
£999.99
Springer Permutation Statistical Methods for Criminology
Book SynopsisIntroduction.- Permutation Statistical Methods.- Central Tendency and Variability.- One-Sample Tests.- Two-Sample Tests.- Matched-Pairs Tests.- Completely-Randomized Designs.- Randomized-Blocks Designs.- Correlation and Association.- Goodness of Fit and Contingency.
£142.49
Springer Time Series and Wavelet Analysis
Book Synopsis- Part I Time Series and Econometrics.- Analysis of High-Frequency Seasonal Time Series.- Stochastic Volatility With Feedback.- Structural Breaks and Common Factors.- A Note About Calibration Tests for VaR and ES.- Dynamic Ordering Learning in Multivariate Forecasting.- A Generalization of the Ornstein-Uhlenbeck Process: Theoretical Results, Simulations and Estimation.- Does the Private Database Help to Explain Brazilian Inflation?.- Identifiability and Whittle Estimation of Periodic ARMA Models.- Dynamic Factor Copulas for Minimum-CVaR Portfolio Optimization.- Part II Wavelets.- Does White Noise Dream of Square Waves?: A Matching Pursuit Conundrum.- Robust Wavelet-based Assessment of Scaling with Applications.- An Overview of Spectral Graph Wavelets.- Statistical Inferences on Brain Functional Networks Using Graph Theory and Multivariate Wavestrapping: An fNIRS Hyperscanning Illustration.- UtilizingWavelet Transform in the Analysis of Scaling Dynamics for Milk Quality Evaluation.- Wavelet Estimation of Nonstationary Spatial Covariance Function.
£151.99
Springer Bayesian Nonparametric Statistics
Book Synopsis
£49.49
Springer Nonlinear Investing A Quantamental Approach
Book SynopsisChapter 1 Introduction.- Chapter 2 Quantamental Analysis.- Chapter 3 Nonlinear Factor Effects on Returns.- Chapter 4 Nonlinear Alpha Modeling.- Chapter 5 Tail Portfolios.- Chapter 6 Nonlinear Investing: Japan Stock Selection Strategy.- Chapter 7 Nonlinear Investing: Currency.- Chapter 9 Nonlinear Investing: Commodity.- Index.
£113.99
Springer International Publishing AG A First Course in Complex Analysis
Book SynopsisThis book introduces complex analysis and is appropriate for a first course in the subject at typically the third-year University level. It introduces the exponential function very early but does so rigorously. It covers the usual topics of functions, differentiation, analyticity, contour integration, the theorems of Cauchy and their many consequences, Taylor and Laurent series, residue theory, the computation of certain improper real integrals, and a brief introduction to conformal mapping. Throughout the text an emphasis is placed on geometric properties of complex numbers and visualization of complex mappings.Table of ContentsPreface.- Acknowledgments.- Basics of Complex Numbers.- Functions of a Complex Variable.- Differentiation.- Contour Integration.- Cauchy Theory.- Series.- Residues.- Conformal Mapping.- Author's Biography.- Index.
£47.49
