Hi, Alex,
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]
Hope this helps,
Bill
----- Original Message -----
From: "Alexander Dekhtyar" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Monday, June 04, 2001 5:34 PM
Subject: [UAI] Bayes Nets tutorial
> Dear Colleagues,
>
> I am putting together a reading list for a student whose research
> will deal, in part, with Bayesian Nets.
>
> I would like to be able to include a short/medium-size tutorial
> that would describe the basic concepts of Bayesian Nets. However,
> at this point, I cannot find any resource that would fit this profile.
> Current options I am aware of are either Judea Pearl's book or
> 1-2 page "Background" sections of various papers. The only other
> alternative is Heckerman's "Learning with Bayes Nets" tutorial, which
> does not seem to be fitting, because we are not interested in
> learning Bayes Nets.
>
> I am wondering if you'd be able to point me at something in between.
>
> Thank you in advance.
>
> - --
> - -------------------------------X----------------------------------
> Alexander Dekhtyar (859) 257 3062 (phone)
> Assistant Professor (859) 323 1971 (fax)
> Department of Computer Science University of Kentucky
> [EMAIL PROTECTED] http://www.cs.uky.edu/~dekhtyar
> - -------------------------------X----------------------------------
=======================================================
William H. Hsu, Ph.D.
Assistant Professor of CIS, Kansas State University
Research Scientist, Automated Learning Group, NCSA
[EMAIL PROTECTED], [EMAIL PROTECTED]
http://www.cis.ksu.edu/~bhsu ICQ: 28651394
=======================================================
