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Statistics - Statistical Theory and Methods | Probability for Statistics and Machine Learning - Fundamentals and Advanced Topics

Probability for Statistics and Machine Learning

Fundamentals and Advanced Topics

DasGupta, Anirban

2011, XX, 784 p.

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  • Unification of probability, statistics, and machine learning tools provides a complete background for teaching and future research inmultiple areas
  • Lucid and encyclopedic coverage allows the user to find and conceptually understand numerous topics by using a single source
  • 1225 worked out examples and exercises provide essential skills in problem solving and help in self-study

This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.

This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

Content Level » Graduate

Keywords » Asymptotics - Boot Strap - Machine Learning - Markov Chain Monte Carlo - Proabability models

Related subjects » Probability Theory and Stochastic Processes - Statistical Theory and Methods - Systems Biology and Bioinformatics - Theoretical Computer Science

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