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.
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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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