Probability for Statistics and Machine Learning

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 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 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 probability and its applications. Particularly worth mentioning are the treatments of distributions.