An Introduction to Probability Theory and Its Applications, Volume II, William Feller

A first course in probability

Probability Theory,Chow Teicher 1997

Schaum's Outline of Probability and Statistics, Third Edition 2009.pdf

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

Feller的权威概率统计教材。

9版英文版，作者Sheldon M.Ross。国外经典教材，包括古典概率，随机过程，排队论，和仿真知识。

一本经典的概率和统计书，英文PDF版。绝对值得珍藏！

Probability and Computing - Randomized Algorithms and Probabilistic Analysis中文版 作者Michael Mitzenmacher， Eli Upfal

Introduction to Probability, 2nd Edition by Dimitri P. Bertsekas and John N. Tsitsiklis, Athina Scientic Press, 2008, 本书的课后习题答案，深入学习很有用。