Counterfactuals and Causal Inference (第二版) Methods and Principles for Social Research。作者：Stephen I. Morgan, Christopher Winship。作为近年来最热话题之一的因果推断分析，这本书将以前以图结构为本的因果分析框架与更加传统的“Potential Outcomes”框架分别以理论和实例进行深度剖析，同时对这二者进行关联与结合，并对其背后的哲学思维与框架限制进行讨论。此书在美国多个顶级院校采用作为教科书，因果分析中的必备材料。

computer vision: models learning and inference（计算机视觉by Simon J. D. Prince） 很棒的计算机视觉基础理论教材，英文版

这是关于统计推理的电子书，高清，最新版本，经典著作，英文版

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Information Theory, Inference, and Learning Algorithms高清pdf，Information Theory, Inference, and Learning Algorithms高清pdf，

《The Elements of Statistical Learning-Data Mining, Inference, and Prediction》英文原版教材第二版

经典的统计学教程，国外考博必备参考书。 非常系统的介绍了统计学知识。

This book requires a strong foundation in signals and systems (see Oppenheim's classic Signals and Systems text, which is used in MIT's 6.003 course) as well as probability (see Bertsekas and Tsitsiklis's text, Introduction to Probability, used for MIT's 6.041 course).

信号与系统最新的教科书，mit教授A. V. Oppenheim的又一力作。介绍了信号与系统分析的基本理论，重点讲述了系统的线性状态空间模型分析、系统的概率模型，假设检验和系统参数估计等内容。将于2016年正式出版。

统计推断的经典教材！