有意思的matlab代码最小概率流学习（MPF） MPF是一种用于非标准化概率模型中参数估计的技术。 该文件中对此进行了描述： J Sohl-Dickstein，P Battaglino，MR德威斯最小概率流学习国际机器学习大会（2011） 该存储库包含为Ising模型和RBM实现MPF的Matlab代码。 目录结构如下： MPF_ising /-Ising模型中的参数估计 MPF_RBM_compare_log_likelihood / -受限玻尔兹曼机器中的参数估计。 该目录还包含将通过伪似然和对比发散训练的小型RBM与通过MPF训练的小型RBM的对数可能性进行比较的代码。 如果您对使用MPF建立神经尖峰数据的Ising模型感兴趣，还应该在查阅Liberty Hamilton的存储库。

Probability and Random Processes with Application to Signal Processing，经典的信号处理教程，英文原版，非扫描版，带部分书签

Feller的权威教材，原先因为不能传大于10M的文件，所以是拆分了上传的，现在作为一个整体传上来。

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。