The probability graph model is a theory that uses graphs to represent the probability dependence of variables . Combining the knowledge of probability theory and graph theory, the graph is used to represent the joint probability distribution of variables related to the model. Developed by the Turing Award winner Pearl. Probabilistic graph model theory is divided into probability graph model representation theory , probability graph model reasoning theory and probability graph model learning theory . In the past 10 years, it has become a research hotspot of uncertainty reasoning, and has broad application prospects in the fields of artificial intelligence, machine learning, and computer vision.
Recommended textbooks and books
- Koller D, Friedman N. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
- Feiyue Wang, Han Suqing's translation of " Probabilistic Graph Model-Principle and Technology "
The above two books are one in English and one is a Chinese version translated by Mr. Wang Feiyue of Chinese Academy of Sciences. This book has more than 1,000 pages and is very comprehensive.
- Christopher M. Bishop. “Pattern Recognition and Machine Learning”. Springer 2006.
Pattern recognition and machine learning in this book are not all about probabilistic graphical models, but part of them.
- J. Pearl. “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference”. Morgan Kaufmann. 1988 .
Finally, this is Judea Pearlthe classic textbook written by the professor.
Summary
Looking at the review is the fastest way to investigate the algorithm, so here are a few review articles.
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Wainwright M J, Jordan M I. Graphical Models, Exponential Families, and Variational Inference. Found. Trends Mach. Learn., 2008, 1:1–305.
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Cheng Qiang et al. Variational Approximate Reasoning Method in Probabilistic Graph Model, Journal of Automation, 2014
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Zhang Hongyi, Wang Liwei, Chen Yuxi. A review of the progress of research on probability graph models, Journal of Software, 2013
Related websites
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Course: Eric Xing : http://www.cs.cmu.edu/~epxing/Class/10708/
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课程：Daphne Koller：https://zh.coursera.org/specializations/probabilistic-graphical-models
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UGM: Matlab code for undirected graphical models：https://www.cs.ubc.ca/~schmidtm/Software/UGM.html
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BNT: Bayes Net Toolbox (MATLAB)：https://code.google.com/archive/p/bnt/
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libDAI (C++)：https://staff.fnwi.uva.nl/j.m.mooij/libDAI/
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OpenGM：http://hciweb2.iwr.uni-heidelberg.de/opengm/
Probability, reasoning and decision-making are the real artificial intelligence.
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