In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.

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.

雷奥奇·卡塞拉、罗杰L.贝耶编著的《统计推断（英文版原书第2版）》从概率论的基础开始，通过例子与习题的旁征博引，引进了大量近代统计处理的新技术和一些国内同类教材中不能见而广为使用的分布。其内容包括工科概率论入门、经典统计和现代统计的基础，又加进了不少近代统计中数据处理的实用方法和思想，例如：Bootstrap再抽样法、刀切（Jackknife）估计、EM算法、Logistic回归、稳健（Robust）回归、Markov链、Monte Carlo方法等。它的统计内容与国内流行的教材相比，理论较深，模型较多，案例的涉及面要广，理论的应用面要丰富，统计思想的阐述与算法更为具体。《统计推断（英文版原书第2版）》可作为工科、管理类学科专业本科生、研究生的教材或参考书，也可供教师、工程技术人员自学之用。

雷奥奇·卡塞拉、罗杰L.贝耶编著的《统计推断（英文版原书第2版）》从概率论的基础开始，通过例子与习题的旁征博引，引进了大量近代统计处理的新技术和一些国内同类教材中不能见而广为使用的分布。其内容包括工科概率论入门、经典统计和现代统计的基础，又加进了不少近代统计中数据处理的实用方法和思想，例如：Bootstrap再抽样法、刀切（Jackknife）估计、EM算法、Logistic回归、稳健（Robust）回归、Markov链、Monte Carlo方法等。它的统计内容与国内流行的教材相比，理论较深，模型较多，案例的涉及面要广，理论的应用面要丰富，统计思想的阐述与算法更为具体。《统计推断（英文版原书第2版）》可作为工科、管理类学科专业本科生、研究生的教材或参考书，也可供教师、工程技术人员自学之用。

Major changes in this edition include the substitution of probabilistic arguments for combinatorial artifices, and the addition of new sections on branching processes, Markov chains, and the De Moivre-Laplace theorem.