章节目录
Chapter 1 Probability. Chapter 2 Random Variables. Chapter 3 Expectation Chapter 4 Inequalities Chapter 5 Convergence of Random Variables Chapter 6 Models, Statistical Inference and Le arning Chapter 7 Estimating the CDF and Statistical Functionals Chapter 8 The Bootstrap Chapter 9 Parametric Inference Chapter 10 Hypothesis Testing and p-values Chapter 11 Bayesian Inference Chapter 12 Statistical Decision Theory Chapter 13 Linear and Logistic Regression Chapter 14 Multivariate Models Chapter 15 Inference about Independence Chapter 16 Causal Inference Chapter 17 Directed Graphs and Conditional Independence Chapter 18 Undirected Graphs Chapter 19 Loglinear Models Chapter 20 Nonparametric Curve Estimation Chapter 21 Smoothing Using Orthogonal Functions Chapter 22 Classification Chapter 23 Probability Redux: Stochastic Processes Chapter 24 Simulation Methods.
内容简介
WINNER OF THE 2005 DEGROOT PRIZE! This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.
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