Probability and statistics Books
Springer Nature Switzerland AG Fundamentals of Data Analytics: With a View to Machine Learning
Book SynopsisThis book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.Table of Contents1 Introduction.- 2 Prerequisites from Matrix Analysis.- 3 Multivariate Distributions and Moments.- 4 Dimensionality Reduction.- 5 Classification and Clustering.- 6 Support Vector Machines.- 7 Machine Learning.- Index.
£54.99
Springer Nature Switzerland AG Mean Field Games: Cetraro, Italy 2019
Book SynopsisThis volume provides an introduction to the theory of Mean Field Games, suggested by J.-M. Lasry and P.-L. Lions in 2006 as a mean-field model for Nash equilibria in the strategic interaction of a large number of agents. Besides giving an accessible presentation of the main features of mean-field game theory, the volume offers an overview of recent developments which explore several important directions: from partial differential equations to stochastic analysis, from the calculus of variations to modeling and aspects related to numerical methods. Arising from the CIME Summer School "Mean Field Games" held in Cetraro in 2019, this book collects together lecture notes prepared by Y. Achdou (with M. Laurière), P. Cardaliaguet, F. Delarue, A. Porretta and F. Santambrogio.These notes will be valuable for researchers and advanced graduate students who wish to approach this theory and explore its connections with several different fields in mathematics.Table of Contents- An Introduction to Mean Field Game Theory. - Lecture Notes on Variational Mean Field Games. - Master Equation for Finite State Mean Field Games with Additive Common Noise. - Mean Field Games and Applications: Numerical Aspects.
£37.49
Springer Nature Switzerland AG Probabilistic Graphical Models: Principles and
Book SynopsisThis fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.Table of ContentsPart I: FundamentalsIntroductionProbability TheoryGraph TheoryPart II: Probabilistic ModelsBayesian ClassifiersHidden Markov ModelsMarkov Random FieldsBayesian Networks: Representation and InferenceBayesian Networks: LearningDynamic and Temporal Bayesian NetworksPart III: Decision ModelsDecision GraphsMarkov Decision ProcessesPartially Observable Markov Decision Processes Part IV: Relational, Causal and Deep ModelsRelational Probabilistic Graphical ModelsGraphical Causal ModelsCausal DiscoveryDeep Learning and Graphical ModelsA: A Python Library for Inference and LearningGlossaryIndex
£54.99
Springer Nature Switzerland AG Riemannian Optimization and Its Applications
Book SynopsisThis brief describes the basics of Riemannian optimization—optimization on Riemannian manifolds—introduces algorithms for Riemannian optimization problems, discusses the theoretical properties of these algorithms, and suggests possible applications of Riemannian optimization to problems in other fields.To provide the reader with a smooth introduction to Riemannian optimization, brief reviews of mathematical optimization in Euclidean spaces and Riemannian geometry are included. Riemannian optimization is then introduced by merging these concepts. In particular, the Euclidean and Riemannian conjugate gradient methods are discussed in detail. A brief review of recent developments in Riemannian optimization is also provided. Riemannian optimization methods are applicable to many problems in various fields. This brief discusses some important applications including the eigenvalue and singular value decompositions in numerical linear algebra, optimal model reduction in control engineering, and canonical correlation analysis in statistics.Trade Review“The author successfully presents all of this varied material using a consistent and modern notation. … The book meticulously provides references with a comprehensive list at the end. It includes information about software libraries that implement Riemannian optimization in MATLAB, Python, R, C++, and Julia. Both the proofs and calculations in the examples are given with sufficient detail using a consistent notation.” (Anders Linnér, Mathematical Reviews, October, 2022)“The book is a very nice introductory reference for students, engineers, and practitioners to get started in the field of Riemannian optimization. … A highlight of the book is that it reviews the most important work in the field and also mentions current research topics. Thus, I also highly recommended it to researchers getting a broad overview of what is currently studied in the field, without being too detailed or theoretical.” (Lena Sembach, SIAM Review, Vol. 64 (2), June, 2022)Table of ContentsIntroduction.- Preliminaries and Overview of Euclidean Optimization.- Unconstrained Optimization on Riemannian Manifolds.- Conjugate Gradient Methods on Riemannian Manifolds.- Applications of Riemannian Optimization.- Recent Developments in Riemannian Optimization.
£54.99
Springer Nature Switzerland AG Point Process Calculus in Time and Space: An
Book SynopsisThis book provides an introduction to the theory and applications of point processes, both in time and in space. Presenting the two components of point process calculus, the martingale calculus and the Palm calculus, it aims to develop the computational skills needed for the study of stochastic models involving point processes, providing enough of the general theory for the reader to reach a technical level sufficient for most applications. Classical and not-so-classical models are examined in detail, including Poisson–Cox, renewal, cluster and branching (Kerstan–Hawkes) point processes.The applications covered in this text (queueing, information theory, stochastic geometry and signal analysis) have been chosen not only for their intrinsic interest but also because they illustrate the theory. Written in a rigorous but not overly abstract style, the book will be accessible to earnest beginners with a basic training in probability but will also interest upper graduate students and experienced researchers.Table of ContentsIntroduction.- Generalities.- Poisson Process on the Line.- Spatial Poisson Processes.- Renewal and Regenerative Processes.- Point Processes with a Stochastic Intensity.- Exvisible Intensity of Finite Point Processes.- Palm Probability on the Line.- Palm Probability in Space.- The Power Spectral Measure.- Information Content of Point Processes.- Point Processes in Queueing.- Hawkes Point Processes.- Appendices.- Bibliography.- Index.
£104.49
Springer Nature Switzerland AG A Course on Small Area Estimation and Mixed
Book SynopsisThis advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians. Table of Contents1 Small Area Estimation.- 2 Design-based Direct Estimation.- 3 Design-based Indirect Estimation.- 4 Prediction Theory.- 5 Linear Models.- 6 Linear Mixed Models.- 7 Nested Error Regression Models.- 8 EBLUPs under Nested Error Regression Models.- 9 Mean Squared Error of EBLUPs.- 10 EBPs under Nested Error Regression Models.- 11 EBLUPs under Two-fold Nested Error Regression Models.- 12 EBPs under Two-fold Nested Error Regression Models.- 13 Random Regression Coefficient Models.- 14 EBPs under Unit-level Logit Mixed Models.- 15 EBPs under Unit-level Two-fold Logit Mixed Models.- 16 Fay-Herriot Models.- 17 Area-level Temporal Linear Mixed Models.- 18 Area-level Spatio-temporal Linear Mixed Models.- 19 Area-level Bivariate Linear Mixed Models.- 20 Area-level Poisson Mixed Models.- 21 Area-level Temporal Poisson Mixed Models.- A Some Useful Formulas.- Index.
£104.49
Springer Nature Switzerland AG Excel 2019 for Environmental Sciences Statistics: A Guide to Solving Practical Problems
Book SynopsisThis book shows the capabilities of Microsoft Excel in teaching environmental science statistics effectively. Similar to the previously published Excel 2016 for Environmental Sciences Statistics, this book is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical environmental science problems. If understanding statistics isn’t the reader’s strongest suit, the reader is not mathematically inclined, or if the reader is new to computers or to Excel, this is the book to start off with.Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in environmental science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Environmental Sciences Statistics: A Guide to Solving Practical Problems capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work.In this new edition, each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand environmental science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned.Table of ContentsPreface.- Acknowledgements.- 1 Sample Size, Mean, Standard Deviation, and Standard Error of the Mean.- 2 Random Number Generator.- 3 Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing.- 4 One-Group t-Test for the Mean.- 5 Two-Group t-Test of the Difference of the Means for Independent Groups.- 6 Correlation and Simple Linear Regression.- 7 Multiple Correlation and Multiple Regression.- 8 One-Way Analysis of Variance (ANOVA).- Appendix A: Answers to End-of-Chapter Practice Problems.- Appendix B: Practice Test.- Appendix C: Answers to Practice Test.- Appendix D: Statistical Formulas.- Appendix E: t-table.- Index.
£64.99
Springer Nature Switzerland AG Luminescence: Data Analysis and Modeling Using R
Book SynopsisThis book covers applications of R to the general discipline of radiation dosimetry and to the specific areas of luminescence dosimetry, luminescence dating, and radiation protection dosimetry. It features more than 90 detailed worked examples of R code fully integrated into the text, with extensive annotations. The book shows how researchers can use available R packages to analyze their experimental data, and how to extract the various parameters describing mathematically the luminescence signals. In each chapter, the theory behind the subject is summarized, and references are given from the literature, so that researchers can look up the details of the theory and the relevant experiments. Several chapters are dedicated to Monte Carlo methods, which are used to simulate the luminescence processes during the irradiation, heating, and optical stimulation of solids, for a wide variety of materials. This book will be useful to those who use the tools of luminescence dosimetry, including physicists, geologists, archaeologists, and for all researchers who use radiation in their research.Table of Contents1. Introduction.- 2. Analysis and Modeling of TL Data.- 3. Analysis of Experimental OSL Data.- 4. Dose Response of Dosimetric Materials.- 5. Monte Carlo Simulations With Fixed Time Interval.- 6. Luminescence as a Stochastic Life-and-Death Process.- 7. Delocalized Transitions: The R Package RLumCarlo.- 8. Localized Transitions: The R Package RLumCarlo.- 9. Quantum Tunneling and Luminescence Models.- 10. Quantum Tunneling: The R Package RLumCarlo.- 11. Comprehensive Quartz Models Using Program KMS.- 12. Quartz Models Using the R-Package RLumModel.
£66.49
Springer Nature Switzerland AG Statistical Foundations, Reasoning and Inference:
Book SynopsisThis textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.Table of ContentsIntroduction.- Background in Probability.- Parametric Statistical Models.- Maximum Likelihood Inference.- Bayesian Statistics.- Statistical Decisions.- Regression.- Bootstrapping.- Model Selection and Model Averaging.- Multivariate and Extreme Value Distributions.- Missing and Deficient Data.- Experiments and Causality.
£94.99
Springer Nature Switzerland AG Undecidability, Uncomputability, and
Book SynopsisFor a brief time in history, it was possible to imagine that a sufficiently advanced intellect could, given sufficient time and resources, in principle understand how to mathematically prove everything that was true. They could discern what math corresponds to physical laws, and use those laws to predict anything that happens before it happens. That time has passed. Gödel’s undecidability results (the incompleteness theorems), Turing’s proof of non-computable values, the formulation of quantum theory, chaos, and other developments over the past century have shown that there are rigorous arguments limiting what we can prove, compute, and predict. While some connections between these results have come to light, many remain obscure, and the implications are unclear. Are there, for example, real consequences for physics — including quantum mechanics — of undecidability and non-computability? Are there implications for our understanding of the relations between agency, intelligence, mind, and the physical world? This book, based on the winning essays from the annual FQXi competition, contains ten explorations of Undecidability, Uncomputability, and Unpredictability. The contributions abound with connections, implications, and speculations while undertaking rigorous but bold and open-minded investigation of the meaning of these constraints for the physical world, and for us as humans.Table of ContentsIntroduction (Aguirre, Merali, Sloan).- Undecidability and Unpredictability: Not Limitations, but Triumphs of Science (Markus Müller).- Indeterminism and Undecidability (Klaas Landsman).- Unpredictability and Randomness (Rade Vuckovac).- Indeterminism, Causality and Information: Has Physics ever been Deterministic? (Flavio Del Santo).- Undecidability, Fractal Geometry and the Unity of Physics (Tim Palmer).- A Gödelian Hunch from Quantum Theory (Hippolyte Dourdent).- Epistemic Horizons: This Sentence is ..... (Jochen Szangolies).- Why is the Universe Comprehensible? (Ian Durham).- Noisy Deductive Reasoning: How Humans Construct Math, and How Math Constructs Universes (David Wolpert, David Kinney).- Computational Complexity as Anthropic Principle: A Fable (Rick Searle).- Appendix (Aguirre, Merali, Sloan).
£64.99
Springer Nature Switzerland AG Undecidability, Uncomputability, and Unpredictability
Book SynopsisFor a brief time in history, it was possible to imagine that a sufficiently advanced intellect could, given sufficient time and resources, in principle understand how to mathematically prove everything that was true. They could discern what math corresponds to physical laws, and use those laws to predict anything that happens before it happens. That time has passed. Gödel’s undecidability results (the incompleteness theorems), Turing’s proof of non-computable values, the formulation of quantum theory, chaos, and other developments over the past century have shown that there are rigorous arguments limiting what we can prove, compute, and predict. While some connections between these results have come to light, many remain obscure, and the implications are unclear. Are there, for example, real consequences for physics — including quantum mechanics — of undecidability and non-computability? Are there implications for our understanding of the relations between agency, intelligence, mind, and the physical world? This book, based on the winning essays from the annual FQXi competition, contains ten explorations of Undecidability, Uncomputability, and Unpredictability. The contributions abound with connections, implications, and speculations while undertaking rigorous but bold and open-minded investigation of the meaning of these constraints for the physical world, and for us as humans.Table of ContentsIntroduction (Aguirre, Merali, Sloan).- Undecidability and Unpredictability: Not Limitations, but Triumphs of Science (Markus Müller).- Indeterminism and Undecidability (Klaas Landsman).- Unpredictability and Randomness (Rade Vuckovac).- Indeterminism, Causality and Information: Has Physics ever been Deterministic? (Flavio Del Santo).- Undecidability, Fractal Geometry and the Unity of Physics (Tim Palmer).- A Gödelian Hunch from Quantum Theory (Hippolyte Dourdent).- Epistemic Horizons: This Sentence is ..... (Jochen Szangolies).- Why is the Universe Comprehensible? (Ian Durham).- Noisy Deductive Reasoning: How Humans Construct Math, and How Math Constructs Universes (David Wolpert, David Kinney).- Computational Complexity as Anthropic Principle: A Fable (Rick Searle).- Appendix (Aguirre, Merali, Sloan).
£64.99
Springer Nature Switzerland AG Multivariate Exponential Families: A Concise Guide to Statistical Inference
Book SynopsisThis book provides a concise introduction to exponential families. Parametric families of probability distributions and their properties are extensively studied in the literature on statistical modeling and inference. Exponential families of distributions comprise density functions of a particular form, which enables general assertions and leads to nice features. With a focus on parameter estimation and hypotheses testing, the text introduces the reader to distributional and statistical properties of multivariate and multiparameter exponential families along with a variety of detailed examples. The material is widely self-contained and written in a mathematical setting. It may serve both as a concise, mathematically rigorous course on exponential families in a systematic structure and as an introduction to Mathematical Statistics restricted to the use of exponential families.Table of ContentsIntroduction.- Parametrizations and Basic Properties.- Distributional and Statistical Properties.- Parameter Estimation.- Hypotheses Testing.- Exemplary Multivariate Applications.
£54.99
Springer Nature Switzerland AG Upper and Lower Bounds for Stochastic Processes:
Book SynopsisThis book provides an in-depth account of modern methods used to bound the supremum of stochastic processes. Starting from first principles, it takes the reader to the frontier of current research. This second edition has been completely rewritten, offering substantial improvements to the exposition and simplified proofs, as well as new results.The book starts with a thorough account of the generic chaining, a remarkably simple and powerful method to bound a stochastic process that should belong to every probabilist’s toolkit. The effectiveness of the scheme is demonstrated by the characterization of sample boundedness of Gaussian processes. Much of the book is devoted to exploring the wealth of ideas and results generated by thirty years of efforts to extend this result to more general classes of processes, culminating in the recent solution of several key conjectures.A large part of this unique book is devoted to the author’s influential work. While many of the results presented are rather advanced, others bear on the very foundations of probability theory. In addition to providing an invaluable reference for researchers, the book should therefore also be of interest to a wide range of readers.Trade Review“The book includes a rich collection of exercises that will allow the reader to gain skills for a better understanding. The book is then suitable as a textbook for an advanced course. … The systematic and deep treatment of the subject under study makes the book a good reference for the specialist.” (Erick Treviño-Aguilar, Mathematical Reviews, March, 2023)Table of Contents1. What is This Book About? Part I The Generic Chaining.- 2 Gaussian Processes and the Generic Chaining.- 3 Trees and Other Measures of Size.- 4 Matching Theorems.- Part II Some Dreams Come True.- 5 Warming Up with p-Stable Processes.- 6 Bernoulli Processes.- 7 Random Fourier Series and Trigonometric Sums.- 8 Partitioning Scheme and Families of Distances.- 9 Peaky Part of Functions.- 10 Proof of the Bernoulli Conjecture.- 11 Random Series of Functions.- 12 Infinitely Divisible Processes.- 13 Unfulfilled Dreams.- Part III Practicing.- 14 Empirical Processes, II.- 15 Gaussian Chaos.- 16 Convergence of Orthogonal Series; Majorizing Measures.- 17 Shor's Matching Theorem.- 18 The Ultimate Matching Theorem in Dimension Three.- 19 Application to Banach Space Theory.- A Discrepancy for Convex Sets.- B Some Deterministic Arguments.- C Classical View of Infinitely Divisible Processes.- D Reading Suggestions.- E Research Directions.- F Solutions of Selected Exercises.- G Comparison with the First Edition.- References.- Index.
£123.49
Springer Nature Switzerland AG Applying Quantitative Bias Analysis to
Book SynopsisThis textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods. As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing: Measurement error pertaining to continuous and polytomous variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.Table of ContentsPart I: Introduction1 Introduction and Objectives1 Introduction 1.2 Nonrandomized Epidemiologic Research 1.3 The Treatment of Uncertainty in Nonrandomized Research 1.4 Objective 1.5 Conclusion 2 A Guide to Implementing Quantitative Bias Analysis 2.1 Introduction 2.2 Reducing Error 2.3 Reducing Error by Design 2.4 Reducing Error in the Analysis 2.5 Quantifying Error 2.6 Evaluating the Potential Value of Quantitative Bias Analysis2.7 Planning for Bias Analysis 2.8 Creating a Data Collection Plan for Bias Analysis 2.9 Creating an Analytic Plan for a Bias Analysis 2.10 Bias Analysis Techniques 2.11 Introduction to Inference 2.12 Conclusion 3 Data Sources for Bias Analysis 3.1 Bias Parameters 3.2 Internal Data Sources 3.3 Selection Bias 3.4 Uncontrolled Confounder 3.5 Information Bias 3.6 Limitations of Internal Validation Studies 3.7 External Data Sources 3.8 Selection Bias 3.9 Uncontrolled Confounder 3.10 Information Bias 3.11 SummaryPart II: Preliminary Methods to Adjust for Systematic Errors 4 Selection Bias 4.1 Introduction 4.2 Definitions and Terms4.3 Motivation for Bias Analysis 4.4 Sources of Data 4.5 Simple Correction for Differential Initial Participation 4.6 Simple Correction for Differential Loss-to-Follow-up4.7 Sensitivity Analysis of the Bias Analysis 4.7 Signed Directed Acyclic Graphs to Estimate the Direction of Bias 5 Uncontrolled Confounders 5.1 Introduction 5.2 Definitions and Terms5.3 Motivation for Bias Analysis 5.4 Sources of Data5.5 Introduction to Simple Bias Analysis 5.6 Implementation of Simple Bias Analysis5.7 Sensitivity Analysis of the Bias Analysis 5.8 Uncontrolled Confounder in the Presence of Effect Modification 5.9 Polytomous Confounders 5.10 Bounding the Bias Limits of Uncontrolled Confounding5.10 Signed Directed Acyclic Graphs to Estimate the Direction of Bias5.11 Uncontrolled Confounding with Continuous Outcome, Exposure, or Confounder 6 Misclassification 6.1 Introduction 6.2 Definitions and Terms6.3 Motivation for Bias Analysis6.4 Sources of Data6.5 Calculating Classification Bias Parameters from Validation Data6.6 Exposure Misclassification for Dichotomous Exposures6.7 Exposure Misclassification for Polytomous Exposures6.8 Disease Misclassification 6.9 Covariate Misclassification 6.10 Dependent Misclassification6.11 Sensitivity Analysis of the Bias Analysis6.12 Adjusting Standard Errors for Corrections 7 Measurement Error for Continuous Variables7.1 Introduction7.2 Definition and Terms7.3 Motivation for Bias Analysis7.4 Exposure Measurement error7.5 Outcome Measurement error7.6 Covariate Measurement Error7.7 Correlated errors 8 Multiple Bias Modeling 8.1 Introduction 8.2 Order of Bias Analyses8.3 Multiple Bias Analysis, Simple MethodsPart III: Methods to Incorporate Systematic and Random Errors 9 Bias Analysis by Simulation for Summary Level Data9.1 Introduction 9.2 Probability Distributions 9.3 Correlated Distributions 9.4 Analytic Approach 9.5 Exposure Misclassification Implementation9.6 Exposure Measurement Error Implementation 9.7 Uncontrolled Confounding Implementation 9.8 Selection Bias Implementation 10 Bias Analysis by Simulation for Record Level Data10.1 Introduction 10.2 Analytic Approach 10.3 Exposure Misclassification Implementation10.4 Exposure Measurement Error Implementation 10.5 Uncontrolled Confounding Implementation 10.6 Selection Bias Implementation 11 Combining Systematic and Random Error11.1 Analytic approximation11.2 Resampling approximation11.3 Bootstrapping 12 Bias Analysis by Missing Data Methods12.1 Introduction 12.2 Analytic Approach 12.3 Exposure Misclassification Implementation12.4 Exposure Measurement Error Implementation 12.5 Uncontrolled Confounding Implementation 12.6 Selection Bias Implementation 12.7 Combining Systematic and Random Error 13 Bias Analysis by Empirical Methods13.1 Introduction 13.2 Analytic Approach 13.3 Exposure Misclassification Implementation 13.4 Exposure Measurement Error Implementation13.5 Uncontrolled Confounding Implementation 13.6 Selection Bias Implementation 13.7 Combining Systematic and Random Error 14 Bias Analysis by Bayesian Methods14.1 Introduction 14.2 Analytic Approach 14.3 Exposure Misclassification Implementation 14.4 Exposure Measurement Error Implementation 14.5 Uncontrolled Confounding Implementation 14.6 Selection Bias Implementation 14.7 Combining Systematic and Random Error 15 Multiple Bias Modeling15.1 Multiple Bias Analysis, Probabilistic Methods15.2 Multiple Bias Analysis, Missing Data Methods15.3 Multiple Bias Analysis, Empirical Methods15.4 Multiple Bias Analysis, Bayesian Methods Part IV: Good Practices16 Good Practices for Quantitative Bias Analysis16.1 Selection of bias sources16.2 Selection of analytic strategies16.3 Selection of values to assign to bias parameters17 Presentation and Inference 17.1 Presentation of simple and multidimensional bias analyses17.2 Presentation of advanced bias analyses 17.3 Inference 17.4 Caveats and Cautions 18 References 19 Index
£54.14
Springer Nature Switzerland AG Uncertainty in Engineering: Introduction to Methods and Applications
Book SynopsisThis open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners.Table of ContentsIntroduction to Bayesian statistical inference.- Sampling from complex probability distributions: a Monte Carlo primer for engineers.- Introduction to the theory of imprecise probability.- Imprecise discrete-time Markov chains.- Statistics with imprecise probabilities – a short survey.- Reliability.- Simulation methods for the analysis of complex systems.- Overview of stochastic model updating in aerospace application under uncertainty treatment.- Aerospace flight modeling and experimental testing.
£21.53
Springer Nature Switzerland AG Business Analytics for Professionals
Book SynopsisThis book explains concepts and techniques for business analytics and demonstrate them on real life applications for managers and practitioners. It illustrates how machine learning and optimization techniques can be used to implement intelligent business automation systems. The book examines business problems concerning supply chain, marketing & CRM, financial, manufacturing and human resources functions and supplies solutions in Python. Table of ContentsPART I: TBA.- Chapter 1. Business Analytics for Managers.- Chapter 2. Big Data Management and Technologies.- Chapter 3. Descriptive Analytics: Feature Engineering & Data Visualization.- Chapter 4. Predictive Analytics with Machine Learning.- Chapter 5. Neural Networks and Deep Learning.- Chapter 6. Handling Unstructured Data: Text Analytics and Image Analysis.- Chapter 7. Prescriptive Analytics: Optimization and Modelling.- PART II: TBA.- Chapter 8. Supply Chain Analytics.- Chapter 9. CRM & Marketing Analytics.- Chapter 10. Financial Analytics.- Chapter 11. Human Resources Analytics.- Chapter 12. Manufacturing Analytics.
£64.99
Springer Nature Switzerland AG Applied Probability: From Random Experiments to
Book SynopsisThis textbook presents the basics of probability and statistical estimation, with a view to applications. The didactic presentation follows a path of increasing complexity with a constant concern for pedagogy, from the most classical formulas of probability theory to the asymptotics of independent random sequences and an introduction to inferential statistics. The necessary basics on measure theory are included to ensure the book is self-contained. Illustrations are provided from many applied fields, including information theory and reliability theory. Numerous examples and exercises in each chapter, all with solutions, add to the main content of the book.Written in an accessible yet rigorous style, the book is addressed to advanced undergraduate students in mathematics and graduate students in applied mathematics and statistics. It will also appeal to students and researchers in other disciplines, including computer science, engineering, biology, physics and economics, who are interested in a pragmatic introduction to the probability modeling of random phenomena.Table of Contents- 1. Events and Probability Spaces. - 2. Random Variables. - 3. Random Vectors. - 4. Random Sequences. - 5. Introduction to Statistics.
£49.99
Springer Data Analysis of Medical Studies
Book SynopsisChapter 1. Data Distribution.- Chapter 2. Graphical Displays of Data Distributions.- Chapter 3. Descriptive Statistics.- Chapter 4. Unpaired Qualitative Data Sets.- Chapter 5. Paired Qualitative Data Sets.- Chapter 6. Unpaired Quantitative Data Sets.- Chapter 7. Paired Quantitative Data Sets.- Chapter 8. Association Between Two Qualitative Variables.- Chapter 9. Relationship Between Two Quantitative Variables.
£85.49
Springer Lectures on Advanced Topics in Categorical Data
Book Synopsis1. Introduction.- 2. Undirected graphical models.- 3. Directed graphical models.- 4. Marginal models: definition.- 5. Marginal log-linear models: applications.- ?6. Path models.- 7. Relational models: definition and interpretation.- 8. Relational models as exponential families.- 9. Relational models: estimation and testing.- 10. Model testing.- 11. The mixture index of fit.
£75.99
Springer Explorations in Monte Carlo Methods
Book Synopsis1. Introduction to Monte Carlo Methods.- 2. Some Probability Distributions and Their Uses.- 3. Markov Chain Monte Carlo.- 4. Random Walks.- 5. Optimization by Monte Carlo Methods.- 6. More on Markov Chain Monte Carlo.- A. Generating Uniform Random Numbers.- B. Perron Frobenius Theorem.- C. Kelly Allocation for Correlated Investments.- D. Donsker's Theorem.- E. Projects.- References.- List of Notation.- Code Index.
£63.99
Springer Sharp Inequalities for Ordered Random Variables in Statistics and Reliability
Book Synopsis- Introduction and Notation.- Analytic Inequalities and Other Tools.- Deterministic Bounds.- General, Symmetric and Life IID Samples.- Sampling from Finite Populations.- IID Samples from Shape Restricted Families.
£132.99
Springer Recent Advances in Econometrics and Statistics
Book Synopsis- Marc Hallin A commented bibliography (from 1972 to 2023).- Part 1: Rank- and depth-based methods.- Daniel Hlubinka, Šárka Hudecová: One-sample location tests based on center-outward signs and ranks.- Jyrki Möttönen, Klaus Nordhausen, Hannu Oja, Una Radojicic: The asymptotic properties of the one-sample spatial rank methods.- Gaspard Bernard, Thomas Verdebout: Power enhancement for testing the equality of shape matrices eigenvalues under ellipticity.- Germain Van Bever, Gaëtan Louvet: The influence function of scatter halfspace depth.- Ramon van den Akker, Bas J.M. Werker, Bo Zhou: Hybrid rank-based panel unit root tests.- Jean-Jacques Droesbeke, Catherine Vermandele: About the relationship between mean and median.- Part 2: Asymptotic statistics.- Christophe Ley, Andreas Anastasiou: How to build optimal tests for normality under possibly singular Fisher information?.- Jean-Marie Dufour, Masaya Takano: Generalized C(alpha) tests with nonstandard convergence rates.- Stéphane Lhaut, Johan Segers: An asymptotic expansion of the empirical angular measure for bivariate extremal dependence.- Paul Deheuvels: Uniform in bandwidth nonparametric kernel estimators of lifetime functionals under random censorship.- Part 3: Quantile regression.- Jana Jureckova: The process induced by slope component of alpha-regression quantile.- Miroslav Šiman: Testing axial symmetry by means of directional quantile regression coefficients.- Manon Felix, Davide La Vecchia, Hang Liu, Yiming Ma: Some novel aspects of quantile regression: local stationarity, random forests and optimal transportation.- Richard K. Crump, Miro Everaert, Domenico Giannone, C. Sean Hundtofte: Changing risk-return profiles.- Part 4: Econometrics.- Marie Hušková, Charl Pretorius: Detection of changes in panel data models with stationary regressors.- Mario Forni, Marco Lippi: Approximating singular by means of non-singular structural VARs.- Pilar Poncela, Esther Ruiz: Common factors and common shocks: A tale of three (close) signal extraction procedures.- Pedro Galeano, Daniel Peña: Detecting outliers in high dimensional time series by dynamic factor models.- Alexei Onatski: The Hallin-Liska criterion through the lens of the random matrix theory.- Alice Brogniaux, Catherine Dehon, Philippe Emplit, Claudia Toma: Gender bias in closed-ended questions with negative points.- Part 5: Statistical Modelling and related topics.- Guy Mélard: Time-dependent time series models: comments on Marc Hallin's early contributions and a pragmatic view on estimation.- Rong Peng, Zudi Lu, Fangsheng Ge: On a location-wide semiparametric analysis of spatio-temporal dynamics of the COVID-19 daily new cases in the UK.- F. Thomas Bruss: Models for studying interactions between human populations and related problems of optimal transport.- Christine De Mol: Multiplicative algorithms for density combination and deconvolution.- Bruno Ebner, Yvik Swan: Independent additive weighted bias distributions and associated goodness-of-fit tests.- Part 6: High-dimensional and Non-Euclidean data.- Jianqing Fan, Zheng Tracy Ke, Koshiki Bose: Higher moment estimation for elliptically-distributed Data: Is it necessary to use a sledgehammer to crack an egg?.- Holger Dette, Jiajun Tang: Pivotal inference for function-on-function linear regression via self-normalization.- Adeline Fermanian, Jiawei Chang, Terry Lyons, Gérard Biau: The insertion method to invert the signature of a path.- Yan Liu, Lan U, Masanobu Taniguchi: Semiparametric empirical likelihood for circular distributions.
£189.99
£170.99
Springer Methodological and Applied Statistics and Demography III
Book SynopsisPreface.- Contributed Sessions.- Efficient Bayesian estimation of spatial Poisson auto-regression with Leroux random effects.- Approximate Inference for the Bayesian Discrepancy Measure for Precise Statistical Hypotheses.- Generalized Additive Mixed Models in medicine a case study on LDL cholesterol in people living with HIV under different antiretroviral regimens.
£189.99
Springer International Publishing AG Heart Rate Variability Analysis with the R package RHRV
Book SynopsisThis book introduces readers to the fundamental concepts of Heart Rate Variability (HRV) and its most important analysis algorithms using a hands-on approach based on the open-source RHRV software. HRV refers to the variation over time of the intervals between consecutive heartbeats. Despite its apparent simplicity, HRV is one of the most important markers of autonomic nervous system activity and it has been recognized as a useful predictor of several pathologies. The book discusses all the basic HRV topics, including the physiological contributions to HRV, clinical applications, HRV data acquisition, HRV data manipulation and HRV analysis using time-domain, frequency-domain, time-frequency, nonlinear and fractal techniques. Detailed examples based on real data sets are provided throughout the book to illustrate the algorithms and discuss the physiological implications of the results. Offering a comprehensive guide to analyzing beat information with RHRV, the book is intended for masters and Ph.D. students in various disciplines such as biomedical engineering, human and veterinary medicine, biology, and pharmacy, as well as researchers conducting heart rate variability analyses on both human and animal data. The second edition of the book has been updated to RHRV version 5.0. This version introduces a functionality to perform heart rate variability analysis on entire populations. This functionality automates and streamlines both the calculation of HRV indices in the time, frequency, and nonlinear domains, as well as the subsequent statistical analysis.
£61.74
Springer Biostatistics in Biopharmaceutical Research and Development
Book SynopsisStatistical Challenges in the Analysis of Biomarker Data.- 2. Evaluating Predictive Accuracy of Prognostic Model for Censored Time-to-Event Data Analysis in Clinical Trials.- Statistical Methods for Accommodating Immortal Time: A Selective Review and Comparison.- Variable selection for partially functional additive Cox Model with interval-censored failure time data.- A Bayesian proportional hazards model to predict patient recruitment in multicenter clinical trials.- GET MORE INFORMATION FROM RECURRENT EVENTS DATA.- Introduction to Patient Preference Studies.- Machine Learning for Precision Medicine and Humanized AI for Future Healthcare.- The Statistical Evaluation of Surrogate Endpoints in Clinical Trials???????.- Treatment Effect Estimation Using Data from Observational and Non-Randomized Studies???????.- Methods for Comparing Two Treatments for a Dichotomous Outcome for a Two-Period Design with Treatment Switching of Control Group Period 1 Non-Responders.- Regression-based estimation of optimal adaptive treatment strategies: Key methods???????.- Vaccine Disease-Prevention Efficacy Studies: Traditional Approaches and New Frontiers???????.- Covariate Adjustment in Analyzing Randomized Clinical Trials: Approaches, Software, and Application???????.- Joint correlated responses and feedback effect with time-dependent covariates.- Distributions and Their Approximations for P-Values.
£170.99
Springer Biostatistics in Biopharmaceutical Research and Development
Book SynopsisBias and Randomization in Clinical Trials: 1980s 2020s 2060s.- The Markov Model for Survival Trials at 35 Years-Old.- Absolute Power Corrupts Absolutely: A Review of the Use of Unconditional Probabilities in the Planning of Clinical Trials.- Design of Clinical Trials with the Desirability of Outcome Ranking Methodology.- Benefit:Risk Assessments during Clinical Trials: A Prediction Approach Using the Desirability of Outcome Ranking (DOOR).- The Power of Integration: How the 2-in-1 Clinical Trial Design is Changing the Future of Drug Development.- A Unified Bayesian Decision Rule-Based Approach for Bayesian Design of Clinical Trials Using Historical Data.- Group Sequential Design Under Non-proportional Hazards: Methodologies and Examples.- Multiple Testing in Group Sequential Design.- Plan per-protocol (PP) causal inference analysis addressing intercurrent events following the targeted learning roadmap.- Maximum Tolerated Imbalance Randomization: Theory and Practice.- Response-adaptive randomization designs based on optimal allocation proportion.
£170.99
Springer International Publishing AG An Introduction to Statistical Data Science
Book SynopsisThis graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the extraction of mathematical models from observed data. Aimed at young scientists, and motivated by their scientific prospects, it provides first principle derivations of various algorithms and procedures, thereby supplying a solid background for their future specialization to diverse fields and applications. The beginning of the book presents the basics of statistical science, with an exposition on linear models. This is followed by an analysis of some numerical aspects and various regularization techniques, including LASSO, which are particularly important for large scale problems. Decision problems are studied both from the classical hypothesis testing perspective and, particularly, from a modern support-vector perspective, in the linear and non-linear context alike. Underlying the book is the Bayesian approach and the Bayesian interpretation of various algorithms and procedures. This is the key to principal components analysis and canonical correlation analysis, which are explained in detail. Following a chapter on nonlinear inference, including material on neural networks, the book concludes with a discussion on time series analysis and estimating their dynamic models. Featuring examples and exercises partially motivated by engineering applications, this book is intended for graduate students in applied mathematics and engineering with a general background in probability and linear algebra.
£116.99
£44.99
Springer LogLinear Models and Logistic Regression
Book SynopsisTwo-Dimensional Tables and Simple Logistic Regression.- Three-Dimensional Tables.- Logistic Regression, Logit Models, and Logistic Discrimination.- Independence Relationships and Graphical Models.- Model Selection Methods and Model Evaluation.- Models for Factors with Quantitative Levels.- Fixed and Random Zeros.- Generalized Linear Models.- The Matrix Approach to Log-Linear Models.- The Matrix Approach to Logit Models.- Maximum Likelihood Theory for Log-Linear Models.- Bayesian Binomial Regression. Exact Conditional Tests. - Correspondence Analysis.
£100.13
Springer Statistical Modeling and Applications
Book Synopsis.- Random Gaussian fields and systems of stochastic partial differential equations..- A Poly-cylindrical Bayesian network for clustering oceanographic data..- A Copula-Based Approach to Statistical Modelling of Solar Irradiance..- Two-sample intraclass correlation coefficient tests for matrix-valued data..- Evolution of the generation and analysis of single imputation synthetic datasets in Statistical Disclosure Control..- Some empirical findings on neural network-based forecasting when subjected to autoregressive resampling..- Enriched lognormal models for income data:A new approach to estimate semi-parametric Gaussian mixtures of regressions with varying mixing proportions..- Computational comparisons of two-component mixtures using Lindley-type models..- Baranchik-type estimators under modified balanced loss functions..- Modelling the movement of a South African cheetah using a hidden Markov model and circular-linear regression.
£170.99
Springer MachineLearning Perspectives of AgentBased Models
Book SynopsisAgent-Based Models and the Economics of Crisis.- The Machine Learning perspective.- Setting up Agent-Based Models of Crisis (Microeconomic Model of Crisis; Virus on a Network Spread Model).- Developing models with Python and R.
£113.99
Springer Stochastic Lagrangian Adaptation
Book SynopsisIntroduction.- Problem Statement.- Asymptotic Maximum Likelihood Identification.- Geometric Results.- Lagrangian Adaptation.- Proof of Theorem 5.2.- Index.
£44.99
Springer Identifiability and Regression Analysis of Biological Systems Models
Book Synopsis- 1. Complex Systems, Data and Inference.- 2. Dynamic Models.- 3. Model Identifiability.- 4. Regression and Variable Selection.- 5. Parameter Estimation using Artificial Intelligence.- 6. R Scripts.
£49.49
Springer International Publishing AG R by Example
Book SynopsisNow in its second edition, R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages.
£75.99
Birkhäuser Classical and Spatial Stochastic Processes
Book SynopsisFinite Markov Chains.- Random walks on finite graphs.- The first appearance of a pattern.- The ruin problem.- The Ehrenfest chain.- The simple symmetric random walk.- Asymmetric and higher dimension random walks.- Discrete time birth and death chains.- Discrete time branching process.- Recurrence on countable spaces.- Stationary distributions on countable spaces.- The Poisson process.- Continuous time birth and death chains.- Continuous time branching processes.- Percolation.- A cellular automaton.- A branching random walk.- The contact process on a homogeneous tree.- Appendix: A little more probability.- Bibliography.- Index.
£52.24
Springer Florence Nightingale David
Book SynopsisIntroduction.- Part I At the Glass Ceiling.- Chapter 1 Deserving (1955-1957).- Part II Up the Hill.- Chapter 2 Growing (1909-1928).- Chapter 3 Learning (1928-1931).- Part III In the Trenches.- Chapter 4 Rising (1931-1938).- Chapter 5 Serving (1939-1946).- Part IV Up the Ladder.- Chapter 6 Renewing (1946-1957).- Chapter 7 Transitioning (1957-1967).- Part V In the New World.- Chapter 8 Arriving (1967-1974).- Chapter 9 Leading (1973-1977).- Part VI Down the Memory Lane.- Chapter 10 Remembering.- Chapter 11 Ending (1977-8).- Part VII Over the Lifetime.- Chapter 12 Summarizing.
£123.49
Springer Statistical Methods for Environmental Mixtures
Book SynopsisPreface.- Chapter 1 Environmental Mixtures.- Chapter 2 Characterizing Environmental Mixtures.- Chapter 3 Regression-Based Approaches for Mixture-Health Associations.- Chapter 4 Mixture Indexing Approaches.- Chapter 5 Flexible Approaches for Complex Settings.- Chapter 6 Additional Topics and Final Remarks.
£85.49
Springer Anomalous Stochastics
Book SynopsisIntroduction.- Fundamental Concepts.- Singular Stochastic Processes.- Non-deterministic Fractals.- Signatures and Causes of Multifractality.- Dispersive Transport and Diffusion.- Fractal Wanderings.- Valley Model of Multifractal Continuous-time Random Wandering on Amorphous Substrates.- Statistics of Extremes.- Limit Theorems on the Stock Market.- Comprehensive Partition Function: A Universal Tool in Multifractality.
£59.99
Springer Statistical Optimal Transport
Book Synopsis1. Optimal Transport.- 2. Estimation of Wasserstein distances.- 3. Estimation of transport maps.- 4. Entropic optimal transport.- 5. Wasserstein gradient flows: theory.-6. Wasserstein gradient flows: applications.- 7. Metric geometry of the Wasserstein space.- 8. Wasserstein barycenters.
£59.99
Springer Data Science Classification and Artificial Intelligence for Modeling Decision Making
Book SynopsisPreface.- Acknowledgements.- G. Afriyie, D. Hughes, A. Nettel Aguirre, N. Li, C. H. Lee, L. M. Lix, and T. Sajobi: A Comparison of Multivariate Mixed Models and Generalized Estimation Equations Models for Discrimination in Multivariate Longitudinal Data.- C. Adela Anton and I. Smith: A Multivariate Functional Data Clustering Method Using Parsimonious Cluster Weighted Models.- J. P. Arroyo-Castro and S. W. Chou-Chen: Unsupervised Detection of Anomaly in Public Procurement Processes.- Z. Aouabed, M. Achraf Bouaoune, V. Therrien, M. Bakhtyari, M. Hijri, and V. Makarenkov: Predicting Soil Bacterial and Fungal Communities at Different Taxonomic Levels Using Machine Learning.- V. Bouranta, G. Panagiotidou and T. Chadjipadelis: Candidates, Parties, Issues and the Political Marketing Strategies: A Comparative Analysis on Political Competition in Greece.- J. Cervantes, M. Monge, and D. Sabater: Predicting Air Pollution in Beijing, China Using Chemical, and Climate Variables.- J. Champagne Gareau, É. Beaudry, and V. Makarenkov: Towards Topologically Diverse Probabilistic Planning Benchmarks: Synthetic Domain Generation for Markov Decision Processes.- P. Chaparala and P. Nagabhushan: Symbolic Data Analysis Framework for Recommendation Systems: SDA-RecSys.- E. Costa, I. Papatsouma, and A. Markos: A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data.- M. Farnia and N. Tahiri: A New Metric to Classify B Cell Lineage Tree.- T. Górecki, M.Krzysko, and W. Wolynski: Applying Classification Methods for Multivariate Functional Data.- K. Moussa Sow and N. Ghazzali: Machine Learning-Based Classification and Prediction to Assess Corrosion Degradation in Mining Pipelines.- G. Nason, D. Salnikov, and M. Cortina-Borja: Modelling Clusters in Network Time Series with an Application to Presidential Elections in the USA.- M. A. Nunez and M. A. Schneider: On the Vapnik-Chervonenkis Dimension and Learnability of the Hurwicz Decision Criterion.- W. Pan and L. Billard: Distributional-based Partitioning with Copulas.- G. Panagiotidou and T. Chadjipadelis: Mapping Electoral Behavior and Political Competition: A Comparative Analytical Framework for Voter Typologies and Political Discourses.- O. Rodríguez Rojas: Riemannian Statistics for Any Type of Data.- A. Roy and F. Montes: Hypothesis Testing of Mean Interval for p-dimensional Interval-valued Data.- M. Solís and A. Hernández: UMAP Projections and the Survival of Empty Space: A Geometric Approach to High-Dimensional Data.- Q. Stier and M. C. Thrun: An Efficient Multicore CPU Implementation of the DatabionicSwarm.
£123.49
Springer Séminaire de Probabilités LII
Book Synopsis- Part I: In Memoriam Dominique Lépingle.- 1. From Martingales to Multivalued Stochastic Differential Equations in Polyhedral Domains: A Tribute to Dominique Lépingle. - Part II: Regular Contributions.- 2. An Example Driven Introduction to Probabilistic Rough Paths.- 3. Fluctuations of Linear Spectral Statistics of Deformed Wigner Matrices.- 4. Brownian Paths in an Affine Weyl Chamber and Littelmann Paths: What is known and What ought to be.- 5. Strong Solutions to Beta-Jacobi Processes.- 6. On the Helmholtz Decomposition for Finite Markov Processes.- 7. One-Dimensional Discrete Gaussian Markov Processes: Harmonic Decomposition of Invariant Boundary Conditions.- 8. Switching Identities by Probabilistic Means.- 9. An Application of Sparre Andersen’s Fluctuation Theorem for Exchangeable and Sign-Invariant Random Variables.- 10. Precise Asymptotics for the Density and the Upper Tail of Exponential Functionals of Subordinators.- 11. Results for Convergence Rates Associated with Renewal Processes.
£62.69
Springer A OneSemester Course on Probability
Book Synopsis- 1. The Beginning.- 2. The General Definition of Probability.- 3. Random Variables and Their Distributions.- 4. Expected Value for Random Variables.- 5. Random Variable Parameters.- 6. Characteristic Functions.- 7. Limit Theorems.- 8. Extension of Measure.
£39.99
Springer Stochastic Geometry Percolation Tesselations Gaussian Fields and Point Processes
Book Synopsis- 1. An Introduction to Russo-Seymour-Welsh Theory.- 2. Random Tessellations - An Overview of Models.- 3. Gaussian Fields through Geometrical Properties.- 4. Complex Gaussian Zeros and Eigenvalues.- 5. Point Processes and Spatial Statistics in Time-Frequency Analysis.
£62.69
Springer Coupling and Ergodic Theorems for SemiMarkovType Processes II
Book SynopsisPreface.- Introduction.- Summary of Ergodic Theorems for Regenerative Processes.- Modifications of Hitting Times.- Birth-Death-Type Processes.- Semi-Markov Processes with Discrete State Spaces and Embedded Regenerative Processes.- Ergodic Theorems for Queuing Systems.- Semi-Markov Processes with General State Spaces with Atoms.- Semi-Markov Processes with General State Spaces and Distributional Atoms.- Semi-Markov Processes with General State Spaces and One-Step Artificial Regeneration.- Semi-Markov Processes with General State Spaces and Multi-Step Artificial Regeneration.- Multi-Alternating Regenerative Processes with Semi-Markov Modulation.- Multi-Alternating Regenerative Processes Modulating by Uniformly Recurrent Semi-Markov Processes.- Appendix A. Methodological and Bibliographical Notes.- References.- Index.
£170.99
£189.99
Springer Handbook of Blockchain Analytics
Book SynopsisIntroduction to Blockchain.- Consensus in Blockchain and Distributed Ledger Systems.- Digital Assets in Blockchains.- Cryptoeconomics.- Blockchain Analytics.- Blockchains and Finance.- Modern Applications.- The Blockchain Ecosystem.
£208.99
Springer Statistics for Innovation III
Book SynopsisPreface.- Contributed Sessions 2.
£189.99
