Probability and statistics Books
Cambridge University Press Prediction Learning and Games
Book SynopsisThis important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that oTrade Review'This book is a comprehensive treatment of current results on predicting using expert advice.' Mathematical ReviewsTable of Contents1. Introduction; 2. Prediction with expert advice; 3. Tight bounds for specific losses; 4. Randomized prediction; 5. Efficient forecasters for large classes of experts; 6. Prediction with limited feedback; 7. Prediction and playing games; 8. Absolute loss; 9. Logarithmic loss; 10. Sequential investment; 11. Linear pattern recognition; 12. Linear classification; 13. Appendix.
£65.54
Cambridge University Press Scattered Data Approximation
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£99.75
Cambridge University Press Selected Statistical Papers of Sir David Cox Volume 1 Design of Investigations Statistical Methods and Applications
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£101.65
Cambridge University Press Selected Statistical Papers of Sir David Cox Volume 2 Foundations of Statistical Inference Theoretical Statistics Time Series and Stochastic Processes
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£101.65
Cambridge University Press Random Networks for Communication From Statistical Physics to Information Systems 24 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 24
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£73.14
Cambridge University Press Lectures on the Combinatorics of Free Probability 335 London Mathematical Society Lecture Note Series Series Number 335
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£96.89
Cambridge University Press Confidence Likelihood Probability
Book SynopsisThis is the first book to develop a methodology of confidence distributions, with a lively mix of theory, illustrations, applications and exercises.Trade Review'This book presents a detailed and wide-ranging account of an approach to inference that moves the discipline towards increased cohesion, avoiding the artificial distinction between testing and estimation. Innovative and thorough, it is sure to have an impact both in the foundations of inference and in a wide range of practical applications of inference.' Nancy Reid, University Professor of Statistical Sciences, University of Toronto'I recommend this book very enthusiastically to any researcher interested in learning more about advanced likelihood theory, based on concepts like confidence distributions and fiducial distributions, and their links with other areas. The book explains in a very didactical way the concepts, their use, their interpretation, etc., illustrated by an impressive number of examples and data sets from a wide range of areas in statistics.' Ingrid Van Keilegom, Université Catholique de LouvainTable of Contents1. Confidence, likelihood, probability: an invitation; 2. Interference in parametric models; 3. Confidence distributions; 4. Further developments for confidence distribution; 5. Invariance, sufficiency and optimality for confidence distributions; 6. The fiducial argument; 7. Improved approximations for confidence distributions; 8. Exponential families and generalised linear models; 9. Confidence distributions in higher dimensions; 10. Likelihoods and confidence likelihoods; 11. Confidence in non- and semiparametric models; 12. Predictions and confidence; 13. Meta-analysis and combination of information; 14. Applications; 15. Finale: summary, and a look into the future.
£72.19
Cambridge University Press Risk Modelling in General Insurance
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£47.49
Cambridge University Press Analysis of GravitationalWave Data 29 Cambridge Monographs on Particle Physics Nuclear Physics and Cosmology Series Number 29
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£86.44
Cambridge University Press Probability and Random Processes for Electrical and Computer Engineers
Book SynopsisThe theory of probability has important applications for computer and electrical engineers as a tool to explain, model, analyse and design the technology they develop. Gubner presents the fundamentals of probability, then progresses to more complicated topics. Suitable for advanced undergraduates, graduates and as a reference for researchers.Trade Review'… stands alone as a textbook that encourages readers to work through and obtain working knowledge of probability and random processes.' IEEE SoftwareTable of ContentsPreface; 1. Introduction to probability; 2. Introduction to discrete random variables; 3. More about discrete random variables; 4. Continuous random variables; 5. Cumulative distribution functions and their applications; 6. Statistics; 7. Bivariate random variables; 8. Introduction to random vectors; 9. Gaussian random vectors; 10. Introduction to random processes; 11. Advanced concepts in random processes; 12. Introduction to Markov chains; 13. Mean convergence and applications; 14. Other modes of convergence; 15. Self similarity and long-range dependence; Bibliography; Index.
£71.24
Cambridge University Press Design of Comparative Experiments 25 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 25
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£103.11
Cambridge University Press Learning Theory An Approximation Theory Viewpoint 24 Cambridge Monographs on Applied and Computational Mathematics Series Number 24
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£71.24
Cambridge University Press Elementary Probability for Applications
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£63.64
Cambridge University Press Secondary Data Sources for Public Health
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£85.49
Cambridge University Press Combinatorics and Probability
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£137.75
Cambridge University Press Saddlepoint Approximations with Applications 22 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 22
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£111.15
Cambridge University Press Statistical Machine Translation
Book SynopsisAutomatic language translation systems like those used by Google, have been revolutionized by recent advances in the methods used in statistical machine translation. This first textbook on the topic explains these innovations carefully and shows the reader, whether a student or a developer, how to build their own translation system.Trade Review'Philipp Koehn has provided the first comprehensive text for the rapidly growing field of statistical machine translation. This book is an invaluable resource for students, researchers, and software developers, providing a lucid and detailed presentation of all the important ideas needed to understand or create a state-of-the-art statistical machine translation system.' Robert C. Moore, Principal Researcher, Microsoft Research'The book primarily represents an ideal introduction to the field of statistical machine translation, but also tackles many of the recent results in this area. It is the product of the many years of both active research and extensive teaching of the author … Each chapter is additionally endowed with a summary, further reading and exercises, achieving thus completely the proposed goal of an accessible introduction to the statistical machine translation field. Apart from its formative role for beginners, the book also stands as a complete guide for researchers in a domain of high interest and rapid expansion … For all these reasons, this book should be welcomed as a highly valuable publication.' Zentralblatt MATH'… Statistical Machine Translation provides an excellent synthesis of a vast amount of literature (the bibliography section takes up 45 double-column pages) and presents it in a well-structured and articulate way. Moreover, the book has been class-tested and contains a set of exercises at the end of each chapter, as well as numerous references to open source tools and resources which enable the diligent reader to build MT systems for any language pair.' Target: International Journal of Translation StudiesTable of ContentsPreface; Part I. Foundations: 1. Introduction; 2. Words, sentences, corpora; 3. Probability theory; Part II. Core Methods: 4. Word-based models; 5. Phrase-based models; 6. Decoding; 7. Language models; 8. Evaluation; Part III. Advanced Topics: 9. Discriminative training; 10. Integrating linguistic information; 11. Tree-based models; Bibliography; Author index; Index.
£59.84
Cambridge University Press Statistical Learning for Biomedical Data
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£104.50
Cambridge University Press From Finite Sample to Asymptotic Methods in Statistics 28 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 29
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£65.54
Cambridge University Press Generalized Linear Models for Insurance Data International Series on Actuarial Science
Book SynopsisActuaries should have the tools they need. Practical and rigorous, this books introduces GLMs in the actuarial context. All techniques are illustrated on data sets relevant to insurance. Exercises and data-based practicals let readers consolidate skills. SAS code and output, data sets, exercise solutions on website.Trade Review"I would recommend such a book to my students without hesitation." Cho-Jieh Chen, Journal of the American Statistical AssociationTable of ContentsPreface; 1. Insurance data; 2. Response distributions; 3. Exponential family responses and estimation; 4. Linear modeling; 5. Generalized linear models; 6. Models for count data; 7. Categorical responses; 8. Continuous responses; 9. Correlated data; 10. Extensions to the Generalized linear model; Appendix 1. Computer code and output; Bibliography; Index.
£88.34
Cambridge University Press Error and Inference
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£55.10
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