文件名称：Building Probabilistic Graphical Models with Python
文件大小：4.32MB
文件格式：PDF
更新时间：2018-05-01 04:10:11
PGM 概率图模型 Python
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.