This book is perfect to get you started with probabilistic graphical models (PGM) with Python. It starts with a quick intro to Bayesian and Markov Networks covering concepts like conditional independence and D-separation. It then covers the different aspects of PGM: structure learning, parameter estimation (with frequentist or Bayesian approach) and inference. All is illustrated with examples and code snippets using mostly the libpgm package. PyMC is used for Bayesian parameter estimation.

LabVIEW graphical programming

英文名：Mit - Probabilistic Graphical Models - Principles and Techniques.pdf， 中文名：概率图模型,概率图论模型 机器学习选用教材

Probabilistic Graphical Models Principles and Techniques。pdf，英文，高清。

Oracle WMS 客户端 配置手册

Windows Python 3.9 • 64-Bit Graphical Installer，下载解压后直接以管理员身份运行，用于在windows下建立自己的机器学习、深度学习工作站

Calculus－Graphical－Numerical－Algebraic－4th－edition AP教材

Learning Probabilistic Graphical Models in R 英文版Learning Probabilistic Graphical Models in R 英文版Learning Probabilistic Graphical Models in R 英文版Learning Probabilistic Graphical Models in R 英文版

讲述了 基于高斯图模型下 对逆稀疏协方差矩阵的估计算法。

LEDA与cgal类似，也是开源的计算几何算法库，相比较而言，leda的源码更直观。