Chapter 6
[6:1] Probabilistic Graphical Models: Overview and Motivation, D. Koller - Stanford University - http://www.youtube.com
[6:2] Introduction to Machine Learning §3.2 Bayesian Decision Theory, E. Alpaydin - MIT Press 2004
[6:3] Machine Learning: A Probabilistic Perspective §10 Directed graphical models, K. Murphy - MIT Press 2012
[6:4] Probabilistic Entity-Relationship Models, PRMs, and Plate Models, D. Heckerman, C. Meek, D. Koller -Stanford University - http://robotics.stanford.edu/~koller/Papers/Heckerman+al:SRL07.pdf
[6:5] Think Bayes Bayesian Statistics Made Simple §1 Bayes's Theorem, A. Downey - Green Tea Press 2010 - http://greenteapress.com/thinkbayes/html/index.html
[6:6] Machine Learning: A Probabilistic Perspective Information §2.8.3 Theory-Mutual Information, K. Murphy – MIT Press 2012
[6:7] Introduction to Information Retrieval §13.2 Naïve Bayes text classification, C.D. Manning, P. Raghavan and H. Schütze, - Cambridge University Press 2008
[6:8] Hidden Naïve Bayes, H. Zhang, J. Su - University of New Brunswick L Jiang University of Geosciences Wuhan, 2004 - http://www.cs.unb.ca/profs/hzhang/publications/AAAI051ZhangH1.pdf
[6:9] Pattern Recognition and Machine Learning §2.3.6 Bayesian inference for the Gaussian, C. Bishop –Springer 2006
[6:10] Pattern Recognition and Machine Learning §2.1 Binary Variables, C. Bishop –Springer 2006
[6:11] Machine Learning Methods in Natural Language Processing, M. Collins - MIT CSAIL -2005 - http://www.cs.columbia.edu/~mcollins/papers/tutorial_colt.pdf
[6:12] Dbpedia: Wikipedia, the free encyclopedia Wikimedia Foundation - http://en.wikipedia.org/wiki/DBpedia
[6:13] Introduction to Information Retrieval §20 Web crawling and indexes, C.D. Manning, P. Raghavan and H. Schütze, - Cambridge University Press, 2008
[6:14] Introduction to Information Retrieval §25 Support vector machines and machine learning on documents, C.D. Manning, P. Raghavan and H. Schütze, - Cambridge University Press, 2008