Publications

     The tutorial program I usually give students consists of
the following:
1. Beginner-level (undergrads, non-CS or non-probabilist):
     Talks
         Breese and Koller's AAAI-97 tutorial
http://www.research.microsoft.com/users/breese/tutorial/
     Reading
         Charniak's "Bayesian Networks Without Tears", AI Magazine 1991
         Cheeseman's "In Defense of Probability", IJCAI 1985
         Pearl's "Reasoning Under Uncertainty", Annual Review of CS 1990 (?)
     Survey web sites
         Kansas State University KDD Lab's Bayesian Network Tools Group
http://groups.yahoo.com/group/kdd-tools
     Software tools
         Hugin (good place to start trying out BN tools)
http://www.hugin.com
         Bayesware (Discoverer, formerly BKD)
http://www.bayesware.com

2. Intermediate (undergrads and grads):
     Talks
         Murphy's tutorial
http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html
         UAI All-Day Course on UR (hard copy)
     Reading
         Neapolitan (Ch. 1-2 general; 3, 6, 7 if working on inference)
Pearl (Ch. 1-2 general, 4 if working on inference; 9 @ other UR)
         Cowell tutorial in Jordan's book
http://www.amazon.com/exec/obidos/ASIN/0262600323
Cheng and Drudzdel's JAIR paper (stochastic sampling @ inference)
         [Jensen's book would go here, but I *still* haven't been
             able to get a copy...]
     Survey web sites
         Guo's BN survey page
http://www.cis.ksu.edu/~hpguo/research/bayes.html
         Santos's BN bibliography
http://excalibur.brc.uconn.edu/~baynet/biblio.html
         Khan's BN survey page
http://www.cs.ust.hk/~samee/bayesian/bayes.html
     Software tools
         Murphy's BN Toolbox (MATLAB)
http://www.cs.berkeley.edu/~murphyk/Bayes/bnt.html
         GeNIe (U. Pittsburgh DSL)
http://www2.sis.pitt.edu/~genie/
         Bayes Online (Welch, Gensym Corp.)
http://www.gensym.com/files/bol/BOL.html

3. Advanced (grads in AI/learning/KDD courses):
     Talks
         Friedman and Goldszmidt's AAAI-98 tutorial
             (if working on learning)
http://robotics.stanford.edu/people/nir/tutorial/index.html
         Heckerman's tutorial
     Reading
         Castillo, Gutierrez, and Hadi
http://www.amazon.com/exec/obidos/ASIN/0387948589
         Cowell et al
http://www.amazon.com/exec/obidos/ASIN/0387987673
         Buntine's tutorial (as a general survey)
         Heckerman's MS-TR-96-05 (as you listed below; only for learning)
     Software tools
         JavaBayes (Cozman's group)
http://www.cs.cmu.edu/~javabayes/Home/

4. Specialized
     Talks (KDD interest)
[anything at AAAI, IJCAI, or UAI on topic of interest @
             learning / inference / decision theory / real-time applications]
[usually some seminar-of-the-month on BNs @ KSU-CIS]
     Reading (caveat - slant towards KDD/DM, ANN)
         Frey 1998 (coding theoretic issues, MCMC methods)
http://www.amazon.com/exec/obidos/ASIN/026206202X
         Neal 1996 (MCMC methods)
http://www.amazon.com/exec/obidos/ASIN/0387947248
         Lauritzen and Spiegelhalter 1998 (exact inference)
         Dagum and Luby (forward sampling / bounded variance)
         Friedman and Yakhini (sample complexity / COLT of BNs)
         Heckerman's MS-TR-96-05 (as you listed below; only for learning)
         Fung and del Favero 1994 (backward simulation)
         Shachter and Peot 1990 (importance sampling - SIS/HIS)
         [other tutorials in Jordan's book]