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.

作品目录

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.