Bayesian Networks
有幾本書(shū)是值得一讀的（都可以下到）：
［1］ (N) Neapolitan, R. E. Learning Bayesian Networks. Pearson Prentice
Hall, 2004
［2］ (RN) Russell, S. and Norvig, P. Artificial Intelligence: A Modern
Approach, 2nd ed. Prentice Hall, 2003
［3］(KN) Korb, K. B. and Nicholson, A. E. Bayesian Artificial
Intelligence. Chapman and Hall/CRC, 2004.
我覺(jué)得（KN）講得比較詳細(xì)。
BN的無(wú)痛入門(mén)：
Charniak, Eugene “Bayesian Networks without Tears”, AI Magazine,
12(4), Winter 91, 50-63
會(huì)議和Murphy的主頁(yè)：
You will also find valuable tutorials, tools, publications on
Bayesian networks and related technologies at the following
websites:
Conference in Uncertainty in Artificial Intelligence (UAI)
???? www.auai.org
American Association for Artificial Intelligence Conference (AAAI)
???? www.aaai.org
International Joint Conference on Artificial Intelligence (IJCAI)
???? www.ijcai.org
Neural Information Processing Systems Conference (NIPS)
???? www.nips.cc
Kevin Murphy’s tutorial on Graphical models and Bayesian networks
http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
兩個(gè)著名的研究BN的工具軟件：
??? Genie 2.0 by Decision Systems Laboratory, University of Pittsburgh
http://genie.sis.pitt.edu/
????? An excellent, free probabilistic networks reasoning program
Netica 4.08 by Norsys Software Corp.
????? Free versions of the main application and the APIs are available
＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞＞
＞＞＞
以下是介紹各個(gè)分支的一些情況：
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Introduction to Bayesian Network:
＠＠＠＠＠＠＠＠＠＠＠＠＠＠＠＠＠
人物：
Judea Pearl as the 2008 Benjamin Franklin Medal in
Computers and Cognitive Science
http://www.fi.edu/franklinawards/08/laureate_bf_computerccs-
pearl.html
Often regarded as the “Father of Bayesian Networks”
Other pioneers in the field:
?? Lauritzen, Steffen L.
?? Spiegelhalter, David J.
?? Neapolitan, Richard E.
?? Jensen, Finn V.
?? ... and many others
Homework 1
＊＊＊＊＊＊＊＊
?? Readings:
1.
??? Charniak, Eugene “Bayesian Networks without Tears”, AI
??? Magazine, 12(4), Winter 91, 50-63
??? N: 1.1-1.2, 1.3 (Italic sections are optional readings)
?? Download and install:
2.
??? Genie 2.0 by Decision Systems Laboratory, University of
??? Pittsburgh http://genie.sis.pitt.edu/
????? An excellent, free probabilistic networks reasoning program
??? Netica 4.08 by Norsys Software Corp.
????? Free versions of the main application and the APIs are available
＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜
＠＠＠＠＠＠＠＠＠＠＠＠＠＠＠＠
Bayesian Network Representation
＠＠＠＠＠＠＠＠＠＠＠＠＠＠＠＠
Readings:
N: 1.4, 2.1 (2.1.2), 2.2, 2.3 (2.3.1), 2.4, 2.5
RN: 14.1-14.2
KN: Chapter 2
R. D. Shachter. “Bayes-ball: The rational pastime.” In
Proceedings of the 14th Conference on Uncertainty in
Artificial Intelligence, pages 480-487, 1998.
＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜
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Exactly Inference
＠＠＠＠＠＠＠＠＠＠＠
N: Chapter 3
KN: Chapter 3
RN: 14.4
JN: Chapter 4
Guo, H. and W. Hsu (2002). A Survey of Algorithms for Real-
Time Bayesian Network Inference. In Proceedings of the Joint
AAAI-02/KDD-02/UAI-02 Workshop on Real-Time Decision
Support and Diagnosis Systems, Edmonton, Alberta, Canada
一定不能少了這伙計(jì)的： Pearl, BN的始祖之一。Judea Pearl as the 2008 Benjamin F
ranklin Medal in
Computers and Cognitive Science http://www.fi.edu/franklinawards/08/laureate_b
f_computerccs-
pearl.html
＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜＜
＠＠＠＠＠＠＠＠＠＠＠
Approximate Inference
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References
RN: 14.5
KN: 3.6
KF: 8.3, 11
Murphy, K. An introduction to graphical models. May 2001. Unpublished
manuscript, available at: http://www.cs.ubc.ca/~murphyk/Papers/intro_gm.pdf
Murphy, K. Software Packages for Graphical Models / Bayesian Networks. Last
updated 28 July 2008. http://www.cs.ubc.ca/~murphyk/Software/bnsoft.html
Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., and Saul, L.K. (1999) An Introd
uction
to Variational Methods for Graphical Models. Machine Learning, Vol. 37, No. 2,
pp.
183-233.
K. Murphy, Y.Weiss, and M. Jordan. Loopy belief propagation for approximate
inference: An empirical study. In UAI'99, pages 467-475, 1999.
Yedidia, J.S.; Freeman, W.T.; Weiss, Y., "Generalized Belief Propagation",
Advances in Neural Information Processing Systems (NIPS), Vol 13, pps 689-695,
December 2000
Jordan和Bishop的材料（Approximate Inference)：
[PRML] Pattern Recognition and Machine Learning 第10章Approximate Inference 主
要講了Variational Method; 這個(gè)方法現(xiàn)在和belief propagation 是approximate infer
ence 中很HOT的兩種方法。
而variational method（變分法） 更是用途廣泛：）
Jordan 派的Graphical Model這個(gè)自然就不用再說(shuō)了。
待續(xù)：）
--
Vi Veri Veniversum Vivus Vici
I by the power of Truth, while living, have conquered the Universe。
以真理的力量 我在死前征服全宇宙
?????????????????????????????????????? －－－－浮士德
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