达芙妮 Koller 2009年大作，不用多说，概率图模型经典，因为是从国外网站购买的高清版pdf，所以有偿分享一下。 （绝对不是扫描板！请放心下载！1270页）

完整版，带目录，机器学习必备经典；大部头要用力啃。 Machine learning A Probabilistic Perspective

加州大学伯克利分校的统计系和计算机科学系的教授迈克尔乔丹2003年所写。是图模型的最新著作，势必成为里程碑式的著作，值得一读。

这真正是 Michael Jordan 写的 An introduction to probabilistic graphic models的草稿

概率图模型是用图来表示变量概率依赖关系的理论，分为概率图模型表示理论，概率图模型推理理论和概率图模型学习理论。在人工智能、机器学习和计算机视觉等领域有广阔的应用前景。

基于MovieLens数据集，采用随机梯度下降算法优化最小化能量函数的概率矩阵分解Python源代码，自己做实验的源代码Probabilistic Matrix Factorization

Probabilistic Graphical Models Principles and Techniques(Koller)

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s resea

《Machine Learning A Probabilistic Perspective》 Kevin P. Murphy著，全英文原版

probabilistic graphical models Jordan draft