Probability and statistics Books
Cambridge University Press God Chance and Purpose Can God Have It Both Ways
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£62.00
Cambridge University Press Modeling and Reasoning with Bayesian Networks
Book SynopsisThis book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.Trade Review'… both practical and advanced … The first five chapters are sufficient for students and practitioners to gain the necessary knowledge in order to build Bayesian networks for moderately sized applications with the aid of a software tool … All major inference methods are covered in later chapters which allow researchers and software developers to implement their own software systems tailored to their needs … It is a comprehensive book that can be used for self study by students and newcomers to the field or as a companion for courses on probabilistic reasoning. Experienced researchers may also find deeper information on some topics. In my opinion, the book should definitely be [on] the bookshelf of everyone who teaches Bayesian networks and builds probabilistic reasoning agents.' Artificial Intelligence'[This] book will make an excellent textbook; it covers topics suitable for both undergraduate and graduate courses. It will also help practitioners get a firm grasp of the fundamentals of modeling and inference with BNs, as well as some recent advances.' ACM Computing ReviewsTable of Contents1. Introduction; 2. Propositional logic; 3. Probability calculus; 4. Bayesian networks; 5. Building Bayesian networks; 6. Inference by variable elimination; 7. Inference by factor elimination; 8. Inference by conditioning; 9. Models for graph decomposition; 10. Most likely instantiations; 11. The complexity of probabilistic inference; 12. Compiling Bayesian networks; 13. Inference with local structure; 14. Approximate inference by belief propagation; 15. Approximate inference by stochastic sampling; 16. Sensitivity analysis; 17. Learning: the maximum likelihood approach; 18. Learning: the Bayesian approach; Appendix A: notation; Appendix B: concepts from information theory; Appendix C: fixed point iterative methods; Appendix D: constrained optimization.
£99.75
Cambridge University Press Partial Differential Equations for Probabilists
Book SynopsisThis book provides probabilists with sufficient background to begin applying PDEs to probability theory and probability theory to PDEs. It covers the theory of linear and second order PDEs of parabolic and elliptic type. While most of the techniques described have antecedents in probability theory, the book does cover a few purely analytic techniques.Trade Review'The book will capture your attention with elegant proofs presented in an almost perfectly self-contained manner, with abundant talk in a lecturer's tone by the author himself, but with a little bit of an aficionado's taste. The book, arranged idiosyncratically, has such a strong impact that, at the next moment, you may find yourself carried away in looking for mathematical treasures scattered here and there in each chapter. The reviewer recommends the present book with confidence to anyone who in interested in PDE and probability theory. At least you should always keep this at your side if you are a probabilist at all.' Isamu Doku, Mathematical ReviewsTable of Contents1. Kolmogorov's forward, basic results; 2. Non-elliptic regularity results; 3. Preliminary elliptic regularity results; 4. Nash theory; 5. Localization; 6. On a manifold; 7. Subelliptic estimates and Hörmander's theorem.
£54.15
Cambridge University Press Bayesian Methods in Cosmology
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£64.59
Cambridge University Press Spatial Analysis for the Social Sciences
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£61.75
Cambridge University Press Price and Quantity Index Numbers
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£58.90
Cambridge University Press Survival Analysis for Epidemiologic and Medical Research Practical Guides to Biostatistics and Epidemiology
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£85.99
Cambridge University Press Analytic Combinatorics
Book SynopsisThe definitive treatment of analytic combinatorics. This self-contained text covers the mathematics underlying the analysis of discrete structures, with thorough treatment of a large number of applications. Exercises, examples, appendices and notes aid understanding: ideal for individual self-study or for advanced undergraduate or graduate courses.Trade Review'… thorough and self-contained … presentation of … topics is very well organised … provides an ample amount of examples and illustrations, as well as a comprehensive bibliography. It is valuable both as a reference work for researchers working in the field and as an accessible introduction suitable for students at an advanced graduate level.' EMS NewsletterTable of ContentsPreface; An invitation to analytic combinatorics; Part A. Symbolic Methods: 1. Combinatorial structures and ordinary generating functions; 2. Labelled structures and exponential generating functions; 3. Combinatorial parameters and multivariate generating functions; Part B. Complex Asymptotics: 4. Complex analysis, rational and meromorphic asymptotics; 5. Applications of rational and meromorphic asymptotics; 6. Singularity analysis of generating functions; 7. Applications of singularity analysis; 8. Saddle-Point asymptotics; Part C. Random Structures: 9. Multivariate asymptotics and limit laws; Part D. Appendices: Appendix A. Auxiliary elementary notions; Appendix B. Basic complex analysis; Appendix C. Concepts of probability theory; Bibliography; Index.
£76.94
Cambridge University Press Probability and Information An Integrated Approach
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£84.55
Cambridge University Press Foundations of Statistical Mechanics
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£17.00
Cambridge University Press Random Matrix Methods for Machine Learning
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£61.74
Cambridge University Press Probability and Inductive Logic
Book SynopsisReasoning from inconclusive evidence, or ''induction'', is central to science and any applications we make of it. For that reason alone it demands the attention of philosophers of science. This element explores the prospects of using probability theory to provide an inductive logic: a framework for representing evidential support. Constraints on the ideal evaluation of hypotheses suggest that the overall standing of a hypothesis is represented by its probability in light of the total evidence, and incremental support, or confirmation, indicated by the hypothesis having a higher probability conditional on some evidence than it does unconditionally. This proposal is shown to have the capacity to reconstruct many canons of the scientific method and inductive inference. Along the way, significant objections are discussed, such as the challenge of inductive scepticism, and the objection that the probabilistic approach makes evidential support arbitrary.
£17.00
Cambridge University Press A Stata Companion for The Fundamentals of Social Research
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£18.24
Cambridge University Press Applied Longitudinal Data Analysis for Medical Science
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£95.00
Cambridge University Press Algorithms for Measurement Invariance Testing
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£17.00
Cambridge University Press The General Linear Model
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£21.84
Cambridge University Press The General Linear Model
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£66.50
Cambridge University Press A Republic If You Can Afford It
Book SynopsisThe cost of administering elections is an importantly understudied area in election science. This book reports election costs in 48 out of 50 states. It discusses the challenges and opportunities of collecting local election costs. The book then presents the wide variation in cost across the country with the lowest spending states spending a little over $2 per voter and the highest spending almost $20 per voter. The amounts being spent in the state are also examined over the election time period of 2008 ? 2016. Economic events like the Great Recession had predictable effects on lowering spending on elections but the patterns are not the same across the different regions of the country. The relationship between spending and election administration outcomes is also explored and finds that the voters'' confidence and perceptions of fraud in elections is associated with the amount spent on election administration.
£17.00
Cambridge University Press Algorithms for Measurement Invariance Testing
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£47.49
Cambridge University Press Foundations of Statistical Mechanics
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£47.49
Cambridge University Press Quantitative and Computational Approaches to Phonology
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£47.49
Cambridge University Press Statistics Explained An Introductory Guide for Life Scientists
Book SynopsisUses a clear and encouraging reader-friendly approach to help students improve their confidence in designing experiments and choosing appropriate statistical tests. Even complex topics are explained clearly, using a pictorial approach with a minimum of formulae and terminology. End-of-chapter exercises, new to this edition, allow self-testing.Trade Review'Every so often, a researcher or teacher comes across a book and exclaims 'I wish I had had a book like this when I started!' … Statistics Explained is such a book. Steve McKillup writes with empathy for students' anxiety about statistics. He replaces complex-looking formulae with graphics and realistic examples. He is a biologist writing for fellow-biologists … [The book] explains why the statistical test is needed before describing the test. Essential features of good survey and experimental design are clearly outlined … This is not 'just another biostatistics textbook'. Its sheer readability will restore confidence to the most anxious student while experienced researchers will savour the clarity of the explanations of the common univariate and multivariate analyses … an ideal core text for anyone teaching or studying biostatistics …' Andrew Boulton, University of New England, Australia'It's remarkable that, after the appearance of many statistics textbooks and statistics computer packages over the years, finally someone has produced a succinct and accessible text that takes a common-sense and appealing approach to the basics of statistical analysis. Complementing Steve McKillup's remarkably lucid explanations is a format which sings pleasingly with clarity. The book progresses in logical fashion through the variety of statistical tests and gives the reader a sound background in the process without the common dizzying confusion. The narrative style and informative approach has made my copy a much-travelled item from my bookshelf to the shores of both undergraduate confusion and postgraduate clarification. However, I always make sure it comes back because it [is] a valued item in my biology toolkit.' Michael Kokkinn, University of South Australia'Statistics Explained is an excellent introduction to statistics for new students and a helpful refresher for more seasoned researchers. The text is quite readable and filled with practical examples for the life sciences.' Erin D. Sheets, University of Minnesota College of Pharmacy'Most exciting perhaps are the topics covered that are not often discussed in introductory textbooks … I have no doubt that Statistics Explained will find a large and appreciative audience among undergraduate biology majors.' The Quarterly Review of BiologyTable of ContentsPreface; 1. Introduction; 2. Doing science: hypotheses, experiments and disproof; 3. Collecting and displaying data; 4. Introductory concepts of experimental design; 5. Doing science responsibly and ethically; 6. Probability helps you make a decision about your results; 7. Probability explained; 8. Using the normal distribution to make statistical decisions; 9. Comparing the means of one and two samples of normally distributed data; 10. Type 1 and Type 2 error, power and sample size; 11. Single factor analysis of variance; 12. Multiple comparisons after ANOVA; 13. Two-factor analysis of variance; 14. Important assumptions of analysis of variance, transformations and a test for equality of variances; 15. More complex ANOVA; 16. Relationships between variables: correlation and regression; 17. Regression; 18. Analysis of covariance; 19. Non-parametric statistics; 20. Non-parametric tests for nominal scale data; 21. Non-parametric tests for ratio, interval or ordinal scale data; 22. Introductory concepts of multivariate analysis; 23. Choosing a test; Appendix: critical values of chi-square, t and F; References; Index.
£69.34
Cambridge University Press Stochastic Processes 33 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 33
Book SynopsisThis comprehensive guide to stochastic processes covers a wide range of topics. Short, readable chapters aim for clarity rather than full generality and hundreds of exercises are included. Pitched at a level accessible to beginning graduate students, it is both a course book and a rich resource for individual readers.Trade Review'The author of this book is well recognized for his long standing and successful work in the area of stochastic processes … this book represents quite well the modern state of the art of the theory of stochastic processes. There are good reasons to strongly recommend the book to graduate and postgraduate students taking an advanced course in stochastic processes.' Jordan M. Stoyanov, Zentralblatt MATHTable of ContentsPreface; 1. Basic notions; 2. Brownian motion; 3. Martingales; 4. Markov properties of Brownian motion; 5. The Poisson process; 6. Construction of Brownian motion; 7. Path properties of Brownian motion; 8. The continuity of paths; 9. Continuous semimartingales; 10. Stochastic integrals; 11. Itô's formula; 12. Some applications of Itô's formula; 13. The Girsanov theorem; 14. Local times; 15. Skorokhod embedding; 16. The general theory of processes; 17. Processes with jumps; 18. Poisson point processes; 19. Framework for Markov processes; 20. Markov properties; 21. Applications of the Markov properties; 22. Transformations of Markov processes; 23. Optimal stopping; 24. Stochastic differential equations; 25. Weak solutions of SDEs; 26. The Ray–Knight theorems; 27. Brownian excursions; 28. Financial mathematics; 29. Filtering; 30. Convergence of probability measures; 31. Skorokhod representation; 32. The space C[0, 1]; 33. Gaussian processes; 34. The space D[0, 1]; 35. Applications of weak convergence; 36. Semigroups; 37. Infinitesimal generators; 38. Dirichlet forms; 39. Markov processes and SDEs; 40. Solving partial differential equations; 41. One-dimensional diffusions; 42. Lévy processes; A. Basic probability; B. Some results from analysis; C. Regular conditional probabilities; D. Kolmogorov extension theorem; E. Choquet capacities; Frequently used notation; Index.
£66.49
Cambridge University Press Stochastic Geometry for Wireless Networks
Book SynopsisCovering point process theory, random geometric graphs and coverage processes, this rigorous introduction to stochastic geometry enables the effective analysis of wireless network performance across all possible network configurations, promoting good design choices for future wireless architectures and protocols that reduce interference effects.Trade Review'This book is a welcome addition to the rapidly developing area of applications of stochastic geometric models to telecommunications.' Ilya S. Molchanov, American Mathematical SocietyTable of ContentsPart I. Point Process Theory: 1. Introduction; 2. Description of point processes; 3. Point process models; 4. Sums and products over point processes; 5. Interference and outage in wireless networks; 6. Moment measures of point processes; 7. Marked point processes; 8. Conditioning and Palm theory; Part II. Percolation, Connectivity and Coverage: 9. Introduction; 10. Bond and site percolation; 11. Random geometric graphs and continuum percolation; 12. Connectivity; 13. Coverage; Appendix: introduction to R.
£85.49
Cambridge University Press Brownian Models of Performance and Control
Book SynopsisDirect and to the point, this book from one of the field's leaders covers Brownian motion and stochastic calculus at the graduate level, and illustrates the use of that theory in various application domains, emphasizing business and economics. The mathematical development is narrowly focused and briskly paced, with many concrete calculations and a minimum of abstract notation. The applications discussed include: the role of reflected Brownian motion as a storage model, queuing model, or inventory model; optimal stopping problems for Brownian motion, including the influential McDonaldâSiegel investment model; optimal control of Brownian motion via barrier policies, including optimal control of Brownian storage systems; and Brownian models of dynamic inference, also called Brownian learning models or Brownian filtering models.Table of Contents1. Brownian motion; 2. Stochastic storage models; 3. Further analysis of Brownian motion; 4. Stochastic calculus; 5. Optimally stopping a Brownian motion; 6. Reflected Brownian motion; 7. Optimal control of Brownian motion; 8. Brownian models of dynamic inference; 9. Further examples; Appendix A. Stochastic processes; Appendix B. Real analysis.
£41.79
Cambridge University Press A Basic Course in Measure and Probability Theory
Book SynopsisOriginating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.Table of ContentsPreface; Acknowledgements; 1. Point sets and certain classes of sets; 2. Measures: general properties and extension; 3. Measurable functions and transformations; 4. The integral; 5. Absolute continuity and related topics; 6. Convergence of measurable functions, Lp-spaces; 7. Product spaces; 8. Integrating complex functions, Fourier theory and related topics; 9. Foundations of probability; 10. Independence; 11. Convergence and related topics; 12. Characteristic functions and central limit theorems; 13. Conditioning; 14. Martingales; 15. Basic structure of stochastic processes; References; Index.
£117.19
Cambridge University Press Statistical Methods for Recommender Systems
Book SynopsisThis book is for researchers and students in statistics, data mining, computer science, machine learning, marketing and also practitioners who implement recommender systems. It provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and state-of-the-art solutions in personalization, explore/exploit, dimension reduction and multi-objective optimization.Trade Review'This book provides a comprehensive guide to state-of-the-art statistical techniques that are used to power recommender systems. … The text is authoritative and well written, with the authors drawing on their extensive experience of researching, implementing and evaluating real-world recommender systems. The book considers the underlying mathematics of the techniques it describes and, as such, is aimed at a readership with a strong background in statistics and cognate subjects. However, while readers without such a background are likely to find the mathematics somewhat challenging, the prose descriptions are highly readable and enable readers to understand the key principles and ideas which underpin the various approaches. This book should be of interest to those involved with recommender systems as well as to those with a broader interest in machine learning.' Patrick Hill, BCS: The Chartered Institute for IT (www.bcs.org)Table of ContentsPart I. Introduction: 1. Introduction; 2. Classical methods; 3. Explore/exploit for recommender problems; 4. Evaluation methods; Part II. Common Problem Settings: 5. Problem settings and system architecture; 6. Most-popular recommendation; 7. Personalization through feature-based regression; 8. Personalization through factor models; Part III. Advanced Topics: 9. Factorization through latent dirichlet allocation; 10. Context-dependent recommendation; 11. Multi-objective optimization.
£45.59
Cambridge University Press Stochastic Processes
Book SynopsisThis definitive textbook provides a solid introduction to stochastic processes, covering both theory and applications. It is written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching, and is accompanied by over 300 exercises, with online solutions for instructors.Table of Contents1. Introduction and review of probability; 2. Poisson processes; 3. Gaussian random vectors and processes; 4. Finite-state Markov chains; 5. Renewal processes; 6. Countable-state Markov chains; 7. Markov processes with countable state spaces; 8. Detection, decisions, and hypothesis testing; 9. Random walks, large deviations, and martingales; 10. Estimation.
£64.59
Cambridge University Press Introduction to Mathematical Portfolio Theory International Series on Actuarial Science
Book SynopsisA concise yet comprehensive guide to the mathematics of portfolio theory from a modelling perspective, with discussion of the assumptions, limitations and implementations of the models as well as the theory underlying them. Aimed at advanced undergraduates, this book can be used for self-study or as a course text.Table of ContentsPreface; 1. Definitions of risk and return; 2. Efficient portfolios: the two-asset case; 3. Portfolios with a risk-free asset; 4. Finding the efficient frontier – the multi-asset case; 5. Single-factor models; 6. Multi-factor models; 7. Introducing utility; 8. Utility and risk aversion; 9. Foundations of utility theory; 10. Maximising long-term growth; 11. Stochastic dominance; 12. Risk measures; 13. The Capital Asset Pricing Model; 14. The arbitrage pricing model; 15. Market efficiency and rationality; 16. Brownian motion and stock price models across time; Appendix A. Matrix algebra; Appendix B. Solutions; References; Index.
£52.24
Cambridge University Press BestWorst Scaling
Book SynopsisThis is the first systematic treatment of the theory and application of best-worst scaling (BWS), an emerging methodology in choice experiments. The three types of best-worst scaling are introduced and explored, and example applications and case studies illustrate how to implement, apply, and analyze the theory across many different disciplines.Trade Review'Best-Worst Scaling (BWS) has emerged as a novel and innovative method for eliciting preferences and understanding choice behavior. This book provides researchers and practitioners with a clear understanding of the origins, theory, and use of BWS and contains interesting case studies from a range of disciplines. This excellent collection of papers also provides a fascinating story of how a new research method moves from initial ideas to adoption by researchers in multiple fields worldwide. It is a must-have reference for current users or those interested in learning about BWS.' W. L. (Vic) Adamowicz, Research Director, Alberta Land Institute, University of Alberta'This book is an important guide for researchers, marketing practitioners, and anyone else who wants to apply the power of best-worst scaling to improve the measurement of preferences and attitudes. Louviere, Flynn, and Marley show how best-worst scaling is easy to use with standard tools and software, and can supersede conventional ratings-based and discrete-choice surveys. With this book, the great benefits of best-worst scaling are now within easy reach of everyone.' Scott D. Brown, University of Newcastle, Australia'This is the definitive source work on best-worst scaling - the method is explained and illustrated by its original developers. A must-have for marketing research practitioners, consultants, and academics interested in the latest advances in stated-choice methods.' Robert J. Meyer, Frederick H. Ecker/Metlife Insurance Professor of Marketing and Co-Director, Wharton Center for Risk Management and Decision Processes, The Wharton School, University of PennsylvaniaTable of ContentsPreface; Acknowledgments; Theory and Methods: 1. Introduction and overview of the book; 2. The BWS object case; 3. The BWS profile case; 4. The BWS multi-profile case; 5. Basic models; 6. Looking forward; Applications - Case 1: 7. BWS object case application: attitudes towards end-of-life care Terry N. Flynn, Elisabeth Huynh and Charles Corke; 8. How consumers choose wine: using best-worst scaling across countries Larry Lockshin and Eli Cohen; 9. Best-worst scaling: an alternative to ratings data Geoffrey N. Soutar, Jillian C. Sweeney and Janet R. McColl-Kennedy; Applications - Case 2: 10. When the ayes don't have it: supplementing an accept/reject DCE with a case 2 best-worst scaling task Richard T. Carson and Jordan J. Louviere; 11. BWS profile case application: preferences for treatment in dentistry Emma McIntosh and Terry N. Flynn; 12. BWS profile case application: preferences for quality of life in Australia Terry N. Flynn and Elisabeth Huynh; Applications - Case 3: 13. The stability of aggregate-level preferences in longitudinal discrete choice experiments Towhidul Islam and Jordan J. Louviere; 14. Case 3 best-worst analysis using delivered pizza and toothpaste examples Bart D. Frischknecht and Jordan J. Louviere; 15. Using alternative-specific DCE designs and best and worst choices to model choices Jordan J. Louviere; References; Subject index; Author index.
£55.09
Cambridge University Press Structured Dependence between Stochastic
Book SynopsisThe relatively young theory of structured dependence between stochastic processes has many real-life applications in areas including finance, insurance, seismology, neuroscience, and genetics. With this monograph, the first to be devoted to the modeling of structured dependence between random processes, the authors not only meet the demand for a solid theoretical account but also develop a stochastic processes counterpart of the classical copula theory that exists for finite-dimensional random variables. Presenting both the technical aspects and the applications of the theory, this is a valuable reference for researchers and practitioners in the field, as well as for graduate students in pure and applied mathematics programs. Numerous theoretical examples are included, alongside examples of both current and potential applications, aimed at helping those who need to model structured dependence between dynamic random phenomena.Trade Review'This is a timely book on an important topic, and it is well written.' John Masson Noble, MathSciNet'The authors follow good traditions, starting with exact definitions, commenting on essential properties, asking appropriate questions, formulating theorems, lemmas or propositions and giving explicit conditions under which complete proofs are provided for the statements.' Jordan M. Stoyanov, zbMATHTable of Contents1. Introduction; Part I. Consistencies: 2. Strong Markov consistency of multivariate Markov families and processes; 3. Consistency of finite multivariate Markov chains; 4. Consistency of finite multivariate conditional Markov chains; 5. Consistency of multivariate special semimartingales; Part II. Structures: 6. Strong Markov family structures; 7. Markov chain structures; 8. Conditional Markov chain structures; 9. Special semimartingale structures Part III. Further Developments: 10. Archimedean survival processes, Markov consistency, ASP structures; 11. Generalized multivariate Hawkes processes; Part IV. Applications of Stochastic Structures: 12. Applications of stochastic structures; Appendix A. Stochastic analysis: selected concepts and results used in this book; Appendix B. Markov processes and Markov families; Appendix C. Finite Markov chains: auxiliary technical framework; Appendix D. Crash course on conditional Markov chains and on doubly stochastic Markov chains; Appendix E. Evolution systems and semigroups of linear operators; Appendix F. Martingale problem: some new results needed in this book; Appendix G. Function spaces and pseudo-differential operators; References; Notation index; Subject index.
£95.95
Cambridge University Press Probability and Computing
Book SynopsisGreatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.Trade Review'As randomized methods continue to grow in importance, this textbook provides a rigorous yet accessible introduction to fundamental concepts that need to be widely known. The new chapters in this second edition, about sample size and power laws, make it especially valuable for today's applications.' Donald E. Knuth, Stanford University, California'Of all the courses I have taught at Berkeley, my favorite is the one based on the Mitzenmacher-Upfal book Probability and Computing. Students appreciate the clarity and crispness of the arguments and the relevance of the material to the study of algorithms. The new second edition adds much important material on continuous random variables, entropy, randomness and information, advanced data structures and topics of current interest related to machine learning and the analysis of large data sets.' Richard M. Karp, University of California, Berkeley'The new edition is great. I'm especially excited that the authors have added sections on the normal distribution, learning theory and power laws. This is just what the doctor ordered or, more precisely, what teachers such as myself ordered!' Anna Karlin, University of Washington'By assuming just an elementary introduction to discrete probability and some mathematical maturity, this book does an excellent job of introducing a great variety of topics to the reader. I especially liked the coverage of the Poisson, exponential, and (multi-variate) normal distributions and how they arise naturally, machine learning, Bayesian reasoning, Cuckoo hashing etc. There is a broad range of exercises, including helpful ones on programming to get a feel for the numerics … This connection to practice is unusual and very commendable … Overall, I would highly recommend this book to anyone interested in probabilistic and statistical foundations as applied to computer science, data science, etc. It can be taught at the senior undergraduate or graduate level to students in computer science, electrical engineering, operations research, mathematics, and other such disciplines.' Frederic Green , SIGACT NewsTable of Contents1. Events and probability; 2. Discrete random variables and expectations; 3. Moments and deviations; 4. Chernoff and Hoeffding bounds; 5. Balls, bins, and random graphs; 6. The probabilistic method; 7. Markov chains and random walks; 8. Continuous distributions and the Polsson process; 9. The normal distribution; 10. Entropy, randomness, and information; 11. The Monte Carlo method; 12. Coupling of Markov chains; 13. Martingales; 14. Sample complexity, VC dimension, and Rademacher complexity; 15. Pairwise independence and universal hash functions; 16. Power laws and related distributions; 17. Balanced allocations and cuckoo hashing.
£47.49
Cambridge University Press Partial Differential Equations for Probabilists 112 Cambridge Studies in Advanced Mathematics Series Number 112
Book SynopsisThis book deals with equations that have played a central role in the interplay between partial differential equations and probability theory. Most of this material has been treated elsewhere, but it is rarely presented in a manner that makes it readily accessible to people whose background is probability theory. Many results are given new proofs designed for readers with limited expertise in analysis. The author covers the theory of linear, second order, partial differential equations of parabolic and elliptic types. Many of the techniques have antecedents in probability theory, although the book also covers a few purely analytic techniques. In particular, a chapter is devoted to the De GiorgiâMoserâNash estimates, and the concluding chapter gives an introduction to the theory of pseudodifferential operators and their application to hypoellipticity, including the famous theorem of Lars Hormander.Trade Review'The book will capture your attention with elegant proofs presented in an almost perfectly self-contained manner, with abundant talk in a lecturer's tone by the author himself, but with a little bit of an aficionado's taste. The book, arranged idiosyncratically, has such a strong impact that, at the next moment, you may find yourself carried away in looking for mathematical treasures scattered here and there in each chapter. The reviewer recommends the present book with confidence to anyone who in interested in PDE and probability theory. At least you should always keep this at your side if you are a probabilist at all.' Isamu Doku, Mathematical ReviewsTable of Contents1. Kolmogorov's forward, basic results; 2. Non-elliptic regularity results; 3. Preliminary elliptic regularity results; 4. Nash theory; 5. Localization; 6. On a manifold; 7. Subelliptic estimates and Hörmander's theorem.
£35.68
Cambridge University Press Core Statistics 6 Institute of Mathematical Statistics Textbooks Series Number 6
Book SynopsisBased on a starter course for beginning graduate students, Core Statistics provides concise coverage of the fundamentals of inference for parametric statistical models, including both theory and practical numerical computation. The book considers both frequentist maximum likelihood and Bayesian stochastic simulation while focusing on general methods applicable to a wide range of models and emphasizing the common questions addressed by the two approaches. This compact package serves as a lively introduction to the theory and tools that a beginning graduate student needs in order to make the transition to serious statistical analysis: inference; modeling; computation, including some numerics; and the R language. Aimed also at any quantitative scientist who uses statistical methods, this book will deepen readers' understanding of why and when methods work and explain how to develop suitable methods for non-standard situations, such as in ecology, big data and genomics.Trade Review'The author keeps this book concise by focusing entirely on topics that are most relevant for scientific modeling via maximum likelihood and Bayesian inference. This makes it an ideal text and handy reference for any math-literate scientist who wants to learn how to build sophisticated parametric models and fit them to data using modern computational approaches. I will be recommending this well-written book to my collaborators.' Murali Haran, Pennsylvania State University'Simon Wood has written a must-read book for the instructor, student, and scholar in search of mathematical rigor, practical implementation, or both. The text is relevant to the likelihoodist and Bayesian alike; it is nicely topped off by instructive problems and exercises. Who thought that a core inference textbook needs to be dry?' Geert Molenberghs, Universiteit Hasselt and KU Leuven, Belgium'Simon Wood's book Core Statistics is a welcome contribution. Wood's considerable experience in statistical matters and his thoughtfulness as a writer and communicator consistently shine through. The writing is compact and neutral, with occasional glimpses of Wood's wry humour. The carefully curated examples, with executable code, will repay imitation and development. I warmly recommend this book to graduate students who need an introduction, or a refresher, in the core arts of statistics.' Andrew Robinson, University of Melbourne'This is an interesting book intended for someone who has already taken an introductory course on probability and statistics and who would like to have a nice introduction to the main modern statistical methods and how these are applied using the R language. It covers the fundamentals of statistical inference, including both theory in a concise form and practical numerical computation.' Vassilis G. S. Vasdekis, Mathematical ReviewsTable of Contents1. Random variables; 2. R; 3. Statistical models and inference; 4. Theory of maximum likelihood estimation; 5. Numerical maximum likelihood estimation; 6. Bayesian computation; 7. Linear models.
£37.37
Cambridge University Press The Surprising Mathematics of Longest Increasing Subsequences 4 Institute of Mathematical Statistics Textbooks Series Number 4
Book SynopsisIn a surprising sequence of developments, the longest increasing subsequence problem, originally mentioned as merely a curious example in a 1961 paper, has proven to have deep connections to many seemingly unrelated branches of mathematics, such as random permutations, random matrices, Young tableaux, and the corner growth model. The detailed and playful study of these connections makes this book suitable as a starting point for a wider exploration of elegant mathematical ideas that are of interest to every mathematician and to many computer scientists, physicists and statisticians. The specific topics covered are the Vershik-KerovâLogan-Shepp limit shape theorem, the BaikâDeiftâJohansson theorem, the TracyâWidom distribution, and the corner growth process. This exciting body of work, encompassing important advances in probability and combinatorics over the last forty years, is made accessible to a general graduate-level audience for the first time in a highly polished presentation.Trade Review'The story of longest monotone subsequences in permutations has been, for six decades, one of the most beautiful in mathematics, ranging from the very pure to the applied and featuring many terrific mathematicians, starting with Erdős–Szekeres's 'happy end theorem' and continuing through the Tracy–Widom distribution and the breakthrough of Baik–Deift–Johansson. With its connections to many areas of mathematics, to the Riemann hypothesis, and to high-energy physics we cannot foresee where the story is heading. Dan Romik tells the tale thus far - and teaches its rich multifaceted mathematics, a blend of probability, combinatorics, analysis, and algebra - in a wonderful way.' Gil Kalai, Hebrew University of Jerusalem'How long is the longest increasing subsequence in a random permutation? This innocent-looking combinatorial problem has surprisingly rich connections to diverse mathematical areas: Poisson processes and Last-passage percolation, growth processes and random matrices, Young diagrams and special functions … Its solution weaves together some highlights of nineteenth- and twentieth-century mathematics, yet continues to have growing impact in the twenty-first. Dan Romik's excellent book makes these exciting developments available to a much wider mathematical audience than ever before. The minimal prerequisites ensure that the reader will also encounter mathematical tools that have stood the test of time and can be applied to many other concrete problems. This is a wonderful story of the unity of mathematics, and Romik's enthusiasm for it shines through.' Yuval Peres, Principal Researcher, Microsoft'This is a marvelously readable book that coaches the reader toward an honest understanding of some of the deepest results of modern analytic combinatorics. It is written in a friendly but rigorous way, complete with exercises and historical sidebars. The central result is the famous Baik-Deift-Johansson theorem that determines the asymptotic distribution of the length of the longest increasing subsequence of a random permutation, but many delicious topics are covered along the way. Anyone who is interested in modern analytic combinatorics will want to study this book. The time invested will be well rewarded - both by enjoyment and by the acquisition of a powerful collection of analytical tools.' Michael Steele, University of Pennsylvania'Mathematics books that concentrate on a problem, rather than on a technique or a subfield, are relatively rare but can be a wonderfully exciting way to dive into research. Here we have the felicitous combination of an extraordinarily fascinating and fruitful problem and a literate tour guide with a terrific eye for the best proof. More like a detective story than a text, this elegant volume shows how a single wise question can open whole new worlds.' Peter Winkler, Dartmouth College'Timely, authoritative, and unique in its coverage …' D. V. Feldman, ChoiceTable of Contents1. Longest increasing subsequences in random permutations; 2. The Baik–Deift–Johansson theorem; 3. Erdős–Szekeres permutations and square Young tableaux; 4. The corner growth process: limit shapes; 5. The corner growth process: distributional results; Appendix: Kingman's subadditive ergodic theorem.
£42.41
Cambridge University Press Cause and Correlation in Biology
Book SynopsisMany problems in biology require an understanding of the relationships among variables in a multivariate causal context. Exploring such cause-effect relationships through a series of statistical methods, this book explains how to test causal hypotheses when randomised experiments cannot be performed. This completely revised and updated edition features detailed explanations for carrying out statistical methods using the popular and freely available R statistical language. Sections on d-sep tests, latent constructs that are common in biology, missing values, phylogenetic constraints, and multilevel models are also an important feature of this new edition. Written for biologists and using a minimum of statistical jargon, the concept of testing multivariate causal hypotheses using structural equations and path analysis is demystified. Assuming only a basic understanding of statistical analysis, this new edition is a valuable resource for both students and practising biologists.Trade ReviewReview of previous edition: '… the perfect introduction to SEM. This book can be used as the primary text in a SEM course given within any discipline, and can be used by scholars and researchers from any area of science.' Structural Equation ModelingReview of previous edition: 'Addressing students and practising biologists, Shipley does a terrific job of making mathematical ideas accessible … Cause and Correlation in Biology is a nontechnical and honest introduction to statistical methods for testing causal hypotheses.' Johan Paulsson, Nature Cell BiologyReview of previous edition: 'I highly recommend the book for those interested in multivariate approaches to biology.' Annals of Botany'Bill Shipley has done an excellent job in tackling the fundamental issue of testing causality in biology and making it accessible to any biology student or scholar. This book is about statistics, but the storytelling is for biologists. When the first edition for this book came out, in 2000, path analyses were not a common tool for biologists. Although the first edition convinced us to use structural equation modelling, this second edition supplies the essential toolbox. This book is the best route to take if you want to master structural equation modelling in biology, and the very good news is that this second edition not only provides updates and extensions, it also offers R codes to run your analyses.' Anne Charmantier, Centre d'Écologie Fonctionnelle et Évolutive (CEFE), Montpellier'For a long time biologists have inferred causation only from carefully designed experiments. Shipley's book broadens horizons by showing how to use observational data to infer whether a causal model is plausible, and to estimate the variation in response due to competing causes.' David Warton, University of New South Wales, SydneyTable of ContentsPreface; 1. Preliminaries; 2. From cause to correlation and back; 3. Sewall Wright, path analysis and d-separation; 4. Path analysis and maximum likelihood; 5. Measurement error and latent variables; 6. The structural equations model; 7. Multigroup models, multilevel models, and corrections for non-independence of observations; 8. Exploration, discovery and equivalence; Index.
£47.49
Cambridge University Press Statistical Data Analysis for the Physical Sciences
Book SynopsisA modern introduction to statistics, this book is ideal for undergraduates in physics. It covers the basic topics as well as advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented.Trade Review'This is a very useful compendium of the statistical techniques used in high energy physics experiments. I would recommend it particularly to undergraduate and PhD students entering for the first time our field.' Riccardo Faccini, Sapienza Università di Roma'This is a very useful book on statistical techniques, suitable for advanced undergraduates or graduate students in the physical sciences. Starting from the basics of probability, the book reviews a range of current techniques in areas like hypothesis testing, assignment of confidence limits and multivariate analysis. Many case studies are presented, including several from the author's own speciality of particle physics, though the book is relevant for any field where careful interpretation of data is needed.' David Ward, University of Cambridge'Adrian Bevan's book is more than just an introduction to statistics. His carefully crafted, well thought out book covers a wide range of topics, from basic concepts up to modern techniques: I highly recommend it.' Francois Le Diberder, CNRS/IN2P3/LAL'This is an excellent text on the [principles] of statistics and their practical applications. It incorporates many useful and instructive examples, both in the text as well as in the exercises at the ends of the chapters … There is a primer on set theory and an exceptionally clear and unbiased discussion of the differences between frequentist and Bayesian approaches. In addition to covering the basics, [the book] also includes a chapter discussing and comparing a wide variety of multivariate tests.' Brian Meadows, University of CincinnatiTable of ContentsPreface; 1. Introduction; 2. Sets; 3. Probability; 4. Visualising and quantifying the properties of data; 5. Useful distributions; 6. Uncertainty and errors; 7. Confidence intervals; 8. Hypothesis testing; 9. Fitting; 10. Multivariate analysis; Appendixes; References; Index.
£20.99
Cambridge University Press Modeling and Reasoning with Bayesian Networks
Book SynopsisThis book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplyingTrade Review'… both practical and advanced … The first five chapters are sufficient for students and practitioners to gain the necessary knowledge in order to build Bayesian networks for moderately sized applications with the aid of a software tool … All major inference methods are covered in later chapters which allow researchers and software developers to implement their own software systems tailored to their needs … It is a comprehensive book that can be used for self study by students and newcomers to the field or as a companion for courses on probabilistic reasoning. Experienced researchers may also find deeper information on some topics. In my opinion, the book should definitely be [on] the bookshelf of everyone who teaches Bayesian networks and builds probabilistic reasoning agents.' Artificial Intelligence'[This] book will make an excellent textbook; it covers topics suitable for both undergraduate and graduate courses. It will also help practitioners get a firm grasp of the fundamentals of modeling and inference with BNs, as well as some recent advances.' ACM Computing ReviewsTable of Contents1. Introduction; 2. Propositional logic; 3. Probability calculus; 4. Bayesian networks; 5. Building Bayesian networks; 6. Inference by variable elimination; 7. Inference by factor elimination; 8. Inference by conditioning; 9. Models for graph decomposition; 10. Most likely instantiations; 11. The complexity of probabilistic inference; 12. Compiling Bayesian networks; 13. Inference with local structure; 14. Approximate inference by belief propagation; 15. Approximate inference by stochastic sampling; 16. Sensitivity analysis; 17. Learning: the maximum likelihood approach; 18. Learning: the Bayesian approach; Appendix A: notation; Appendix B: concepts from information theory; Appendix C: fixed point iterative methods; Appendix D: constrained optimization.
£56.99
Cambridge University Press Probability on Graphs
Book SynopsisThis introduction to some of the principal models in the theory of disordered systems leads the reader through the basics, to the very edge of contemporary research, with the minimum of technical fuss. Topics covered include random walk, percolation, self-avoiding walk, interacting particle systems, uniform spanning tree, random graphs, as well as the Ising, Potts, and random-cluster models for ferromagnetism, and the Lorentz model for motion in a random medium. This new edition features accounts of major recent progress, including the exact value of the connective constant of the hexagonal lattice, and the critical point of the random-cluster model on the square lattice. The choice of topics is strongly motivated by modern applications, and focuses on areas that merit further research. Accessible to a wide audience of mathematicians and physicists, this book can be used as a graduate course text. Each chapter ends with a range of exercises.Table of ContentsPreface; 1. Random walks on graphs; 2. Uniform spanning tree; 3. Percolation and self-avoiding walk; 4. Association and influence; 5. Further percolation; 6. Contact process; 7. Gibbs states; 8. Random-cluster model; 9. Quantum Ising model; 10. Interacting particle systems; 11. Random graphs; 12. Lorentz gas; References; Index.
£35.14
Cambridge University Press Probability
Book SynopsisThis lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for applications, this comprehensive treatment is a rigorous graduate text and reference. Operating under the philosophy that the best way to learn probability is to see it in action, the book contains extended examples that apply the theory to concrete applications. This fifth edition contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations (PDEs), an advanced topic that is finding new applications. Setting the foundation for this expansion, Chapter 7 now features a proof of Itô''s formula. Key exercises that previously were simply proofs left to the reader have been directly inserted into the text as lemmas. The new edition re-instates discussion about the central limit theorem for Trade Review'Probability: Theory and Examples 5th Edition still holds true to its original goal that as the theory is developed, the focus of attention will be on examples with hundreds of examples provided and hundreds of example problems given as exercises for the reader.' Brent Kelderman, MAA ReviewsTable of Contents1. Measure theory; 2. Laws of large numbers; 3. Central limit theorems; 4. Martingales; 5. Markov chains; 6. Ergodic theorems; 7. Brownian motion; 8. Applications to random walk; 9. Multidimensional Brownian motion; Appendix. Measure theory details.
£63.64
Cambridge University Press Algorithmic Randomness
Book SynopsisThe last two decades have seen a wave of exciting new developments in the theory of algorithmic randomness and its applications to other areas of mathematics. This volume surveys much of the recent work that has not been included in published volumes until now. It contains a range of articles on algorithmic randomness and its interactions with closely related topics such as computability theory and computational complexity, as well as wider applications in areas of mathematics including analysis, probability, and ergodic theory. In addition to being an indispensable reference for researchers in algorithmic randomness, the unified view of the theory presented here makes this an excellent entry point for graduate students and other newcomers to the field.Table of Contents1. Key developments in algorithmic randomness Johanna N. Y. Franklin and Christopher P. Porter; 2. Algorithmic randomness in ergodic theory Henry Towsner; 3. Algorithmic randomness and constructive/computable measure theory Jason Rute; 4. Algorithmic randomness and layerwise computability Mathieu Hoyrup; 5. Relativization in randomness Johanna N. Y. Franklin; 6. Aspects of Chaitin's Omega George Barmpalias; 7. Biased algorithmic randomness Christopher P. Porter; 8. Higher randomness Benoit Monin; 9. Resource bounded randomness and its applications Donald M. Stull; Index.
£117.19
Cambridge University Press Counterexamples in Measure and Integration
Book SynopsisThis is a perfect companion to any course on measure theory, integration, real and functional analysis, providing more than 300 examples and counterexamples to the otherwise often rather theoretical courses. By knowing 'what may go wrong' students will gain a better understanding of the standard course material.Trade Review'This book is an admirable counterpart, both to the first author's well-known text Measures, Integrals and Martingales (Cambridge, 2005/2017), and to the books on counter-examples in analysis (Gelbaum and Olmsted), topology (Steen and Seebach) and probability (Stoyanov). To paraphrase the authors' preface: in a good theory, it is valuable and instructive to probe the limits of what can be said by investigating what cannot be said. The task is thus well-conceived, and the execution is up to the standards one would expect from the books of the first author and of their papers. I recommend it warmly.' N. H. Bingham, Imperial College'… an excellent reference text and companion reader for anyone interested in deepening their understanding of measure theory.' John Ross, MAA Reviews'… the unique nature of the book makes it an essential acquisition for any university with a doctoral program in pure mathematics … Essential.' M. Bona, Choice Connect'The book is well written, the demonstrations are clear and the bibliographic references are competent. We appreciate this work as extremely useful for those interested in measure theory and integration, starting with beginners and extending even to advanced researchers in the field.' Liviu Constantin Florescu, Mathematical Reviews/MathSciNet'Counterexamples in Measure and Integration is an ideal companion to help better understand canonically problematic examples in analysis … This collection of counterexamples is an excellent resource to researchers who rely on measure and integration theory. It would be helpful for students studying for their analysis qualifying exam as it draws on common misconceptions and enables readers to build intuition about why a given counterexample works and how conditions can be changed to make a particular statement hold.' Katelynn Kochalski, Notices of the AMSTable of ContentsPreface; User's guide; List of topics and phenomena; 1. A panorama of Lebesgue integration; 2. A refresher of topology and ordinal numbers; 3. Riemann is not enough; 4. Families of sets; 5. Set functions and measures; 6. Range and support of a measure; 7. Measurable and non-measurable sets; 8. Measurable maps and functions; 9. Inner and outer measure; 10. Integrable functions; 11. Modes of convergence; 12. Convergence theorems; 13. Continuity and a.e. continuity; 14. Integration and differentiation; 15. Measurability on product spaces; 16. Product measures; 17. Radon–Nikodým and related results; 18. Function spaces; 19. Convergence of measures; References; Index.
£104.50
Cambridge University Press Applied Stochastic Differential Equations
Book SynopsisStochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic RungeKutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book''s practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-Trade Review'Stochastic differential equations have long been used by physicists and engineers, especially in filtering and prediction theory, and more recently have found increasing application in the life sciences, finance and an ever-increasing range of fields. The authors provide intended users with an intuitive, readable introduction and overview without going into technical mathematical details from the often-demanding theory of stochastic analysis, yet clearly pointing out the pitfalls that may arise if its distinctive differences are disregarded. A large part of the book deals with underlying ideas and methods, such as analytical, approximative and computational, which are illustrated through many insightful examples. Linear systems, especially with additive noise and Gaussian solutions, are emphasized, though nonlinear systems are not neglected, and a large number of useful results and formulas are given. The latter part of the book provides an up to date survey and comparison of filtering and parameter estimation methods with many representative algorithms, and culminates with their application to machine learning.' Peter Kloeden, Johann Wolfgang Goethe-Universität Frankfurt am Main'Overall, this is a very well-written and excellent introductory monograph to SDEs, covering all important analytical properties of SDEs, and giving an in-depth discussion of applied methods useful in solving various real-life problems.' Igor Cialenco, MathSciNet'Chapters are rich in examples, numerical simulations, illustrations, derivations and computational assignment' Martin Ondreját, the European Mathematical Society and the Heidelberg Academy of Sciences and HumanitiesTable of Contents1. Introduction; 2. Some background on ordinary differential equations; 3. Pragmatic introduction to stochastic differential equations; 4. Ito calculus and stochastic differential equations; 5. Probability distributions and statistics of SDEs; 6. Statistics of linear stochastic differential equations; 7. Useful theorems and formulas for SDEs; 8. Numerical simulation of SDEs; 9. Approximation of nonlinear SDEs; 10. Filtering and smoothing theory; 11. Parameter estimation in SDE models; 12. Stochastic differential equations in machine learning; 13. Epilogue.
£35.14
HarperCollins Publishers Inc The Upside of Irrationality
Book SynopsisNew York Times Bestseller“Dan Ariely is a genius at understanding human behavior: no economist does a better job of uncovering and explaining the hidden reasons for the weird ways we act.” — James Surowiecki, author of The Wisdom of Crowds Behavioral economist and New York Times bestselling author of Predictably Irrational Dan Ariely offers a much-needed take on the irrational decisions that influence our dating lives, our workplace experiences, and our temptation to cheat in any and all areas. Fans of Freakonomics, Survival of the Sickest, and Malcolm Gladwell’s Blink and The Tipping Point will find many thought-provoking insights in The Upside of Irrationality.How can large bonuses sometimes make CEOs less productive?Why is revenge so important to us?How can confusing directions actually help us?
£16.14
HarperCollins Publishers Inc The End of Average
Book Synopsis
£15.29
HarperCollins Publishers Inc The Hot Hand
Book Synopsis
£24.38
Elsevier Science Statistical Methods
Book Synopsis
£97.72
Elsevier Science Publishing Co Inc Handbook of Statistical Analysis and Data Mining
Book SynopsisTrade Review"Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering, and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here. "Going beyond its responsibility as a reference book, the heavily-updated second edition also provides all-new, detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success. "What's more, this edition drills down on hot topics across seven new chapters, including deep learning and how to avert "b---s---" results. If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner." --Eric Siegel, Ph.D., founder of Predictive Analytics World and author of "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" "Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners." --Karl Rexer, PhD (President and Founder of Rexer Analytics, Boston, Massachusetts)Table of ContentsPart 1: History Of Phases Of Data Analysis, Basic Theory, And The Data Mining Process 1. The Background for Data Mining Practice 2. Theoretical Considerations for Data Mining 3. The Data Mining and Predictive Analytic Process 4. Data Understanding and Preparation 5. Feature Selection 6. Accessory Tools for Doing Data Mining Part 2: The Algorithms And Methods In Data Mining And Predictive Analytics And Some Domain Areas 7. Basic Algorithms for Data Mining: A Brief Overview 8. Advanced Algorithms for Data Mining 9. Classification 10. Numerical Prediction 11. Model Evaluation and Enhancement 12. Predictive Analytics for Population Health and Care 13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors 14. Customer Response Modeling 15. Fraud Detection Part 3: Tutorials And Case Studies Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13 Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) Tutorial C Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data Tutorial E Feature Selection in KNIME Tutorial F Medical/Business Tutorial Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F Tutorial H Data Prep 1-1: Merging Data Sources Tutorial I Data Prep 1–2: Data Description Tutorial J Data Prep 2-1: Data Cleaning and Recoding Tutorial K Data Prep 2-2: Dummy Coding Category Variables Tutorial L Data Prep 2-3: Outlier Handling Tutorial M Data Prep 3-1: Filling Missing Values With Constants Tutorial N Data Prep 3-2: Filling Missing Values With Formulas Tutorial O Data Prep 3-3: Filling Missing Values With a Model Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner Tutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10 Tutorial R Example With C&RT to Predict and Display Possible Structural Relationships Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client Part 4: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and the Future, and Advanced Processes 16. The Apparent Paradox of Complexity in Ensemble Modeling 17. The "Right Model" for the "Right Purpose": When Less Is Good Enough 18. A Data Preparation Cookbook 19. Deep Learning 20. Significance versus Luck in the Age of Mining: The Issues of P-Value "Significance" and "Ways to Test Significance of Our Predictive Analytic Models" 21. Ethics and Data Analytics 22. IBM Watson
£75.04
