Information Theory, Inference and Learning Algorithms

Author: David J. C. MacKay

Publisher: Cambridge University Press

ISBN: 9780521642989

Category: Computers

Page: 628

View: 9229

Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
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Bayesian Reasoning and Machine Learning

Author: David Barber

Publisher: Cambridge University Press

ISBN: 0521518148

Category: Computers

Page: 697

View: 2448

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
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Machine Learning

A Probabilistic Perspective

Author: Kevin P. Murphy

Publisher: MIT Press

ISBN: 0262018020

Category: Computers

Page: 1067

View: 2148

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
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Elements of Information Theory

Author: Thomas M. Cover,Joy A. Thomas

Publisher: John Wiley & Sons

ISBN: 1118585771

Category: Computers

Page: 792

View: 4912

The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
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Power

Author: Alan Blackwell,David MacKay

Publisher: Cambridge University Press

ISBN: 9781139445597

Category: Science

Page: N.A

View: 7329

In this book, first published in 2006, seven internationally renowned writers address the theme of Power from the perspective of their own disciplines. Energy expert Mary Archer begins with an exploration of the power sources of our future. Astronomer Neil Tyson leads a tour of the orders of magnitude in the cosmos. Mathematician and inventor of the Game of Life John Conway demonstrates the power of simple ideas in mathematics. Screenwriter Maureen Thomas explains the mechanisms of narrative power in the media of film and videogames, Elisabeth Bronfen the emotional power carried by representations of life and death, and Derek Scott the power of patriotic music and the mysterious Mozart effect. Finally, celebrated parliamentarian Tony Benn critically assesses the reality of power and democracy in society.
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Information, Mechanism and Meaning

Author: Donald MacCrimmon MacKay

Publisher: Mit Press

ISBN: 9780262630320

Category: Computers

Page: 196

View: 9651

A collection of selected papers written by the information theorist and "brain physicist," most of which were presented to various scientific conferences in the 1950s and 1960s. Most of this collection concerns MacKay's abiding preoccupation with information as represented and utilized in the brain and exchanged between human beings, rather than as formalized in logical patterns of elementary propositions.
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Information Theory and Statistical Learning

Author: Frank Emmert-Streib,Matthias Dehmer

Publisher: Springer Science & Business Media

ISBN: 0387848150

Category: Computers

Page: 439

View: 4751

This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.
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Information Theory and Statistics

Author: Solomon Kullback

Publisher: Courier Corporation

ISBN: 0486142043

Category: Mathematics

Page: 416

View: 1175

Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and problems. References. Glossary. Appendix. 1968 2nd, revised edition.
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Pattern Recognition and Machine Learning

Author: Christopher M. Bishop

Publisher: Springer

ISBN: 9781493938438

Category: Computers

Page: 738

View: 3276

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
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Prediction, Learning, and Games

Author: Nicolo Cesa-Bianchi,Gabor Lugosi

Publisher: Cambridge University Press

ISBN: 113945482X

Category: Computers

Page: N.A

View: 1961

This 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 often reveals new and intriguing connections.
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Information Theory and Evolution

Author: John Avery

Publisher: World Scientific

ISBN: 9814401242

Category: Computers

Page: 264

View: 423

Information Theory and Evolution discusses the phenomenon of life, including its origin and evolution (and also human cultural evolution), against the background of thermodynamics, statistical mechanics, and information theory. Among the central themes is the seeming contradiction between the second law of thermodynamics and the high degree of order and complexity produced by living systems. This paradox has its resolution in the information content of the Gibbs free energy that enters the biosphere from outside sources, as the author will show. The role of information in human cultural evolution is another focus of the book. The first edition of Information Theory and Evolution made a strong impact on thought in the field by bringing together results from many disciplines. The new second edition offers updated results based on reports of important new research in several areas, including exciting new studies of the human mitochondrial and Y-chromosomal DNA. Another extensive discussion featured in the second edition is contained in a new appendix devoted to the relationship of entropy and Gibbs free energy to economics. This appendix includes a review of the ideas of Alfred Lotka, Frederick Soddy, Nicholas Georgiescu-Roegen and Herman E. Daly, and discusses the relevance of these ideas to the current economic crisis. The new edition discusses current research on the origin of life, the distinction between thermodynamic information and cybernetic information, new DNA research and human prehistory, developments in current information technology, and the relationship between entropy and economics.
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Statistical Mechanics of Learning

Author: A. Engel,C. Van den Broeck

Publisher: Cambridge University Press

ISBN: 9780521774796

Category: Computers

Page: 329

View: 5911

Artificial neural networks, learning, statistical mechanics; background material in mathematics and physics; examples and exercises; textbook/reference.
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Information Theory

A Tutorial Introduction

Author: JV Stone

Publisher: Sebtel Press

ISBN: 0956372856

Category: Information theory

Page: 243

View: 725

Originally developed by Claude Shannon in the 1940s, information theory laid the foundations for the digital revolution, and is now an essential tool in telecommunications, genetics, linguistics, brain sciences, and deep space communication. In this richly illustrated book, accessible examples are used to introduce information theory in terms of everyday games like ‘20 questions’ before more advanced topics are explored. Online MatLab and Python computer programs provide hands-on experience of information theory in action, and PowerPoint slides give support for teaching. Written in an informal style, with a comprehensive glossary and tutorial appendices, this text is an ideal primer for novices who wish to learn the essential principles and applications of information theory.
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Bayesian Logical Data Analysis for the Physical Sciences

A Comparative Approach with Mathematica® Support

Author: Phil Gregory

Publisher: Cambridge University Press

ISBN: 113944428X

Category: Mathematics

Page: N.A

View: 2491

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.
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Unsupervised Learning

Foundations of Neural Computation

Author: Geoffrey E. Hinton,Terrence Joseph Sejnowski

Publisher: MIT Press

ISBN: 9780262581684

Category: Computers

Page: 398

View: 9210

Introduction; Unsupervised learning; Local synaptic learning rules suffice to maximize mutual information in a linear network; Convergent algorithm for sensory receptive field development; Emergence of position-independent detectors of snese of rotation and dilation with hebbian learning: an analysis; Learning invariance from transformation sequences; Learning perceptually salient visual parameters using spatiotemporal smoothness constraints; Wht is the goal of sensory coding?; An information-maximization approach to blind separation and blid deconvolution; Natural gradient works efficiently in learning; A fast fixed-point algorithm for independent component analysis; Feature extraction using an unsupervised neural network; Learning mixture models of spatial coherence; Baynesian self-organization driven byprior probability distributions; Finding minimum entropy codes; Learning population codes by minimizing description lengththe Helmholtz machine; factor analysis using delta-rule wake-sleep learning; Dimension reduction by local principal component analysis; A resource-allocating network for function interpolation; 20. Learning with preknowledge: clustering with point and graph matching distance measures; 21. Learning to generalize from single examples in the dynamic ling architecture; Index.
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Pattern Recognition and Neural Networks

Author: Brian D. Ripley

Publisher: Cambridge University Press

ISBN: 9780521717700

Category: Computers

Page: 403

View: 2273

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.
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Computer Age Statistical Inference

Algorithms, Evidence, and Data Science

Author: Bradley Efron,Trevor Hastie

Publisher: Cambridge University Press

ISBN: 1108107958

Category: Mathematics

Page: N.A

View: 765

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
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