BenefitArchival < Main

Main
Main.BenefitArchivalr1.1 - 30 Nov 2005 - 05:08 - MarkCrowleytopic end
---+ Old Resources List
   * [[WhyUseBayesNetsArchival][Why Use Bayes Nets?]]
   * [[#TutSurvey][AUAI Tutorials and Survey Sources]] 
   * [[#InfoServ][Related Information Servers]] 
   * [[UAIGroupsArchival][UAI Related Companies and Research Groups]]

 
#TutSurvey
---++ AUAI Tutorials and Survey Sources
Tutorials are nominated by present and former members of the UAI program committee. Listed by first author. 

   *  <A HREF="http://www.cs.orst.edu/~dambrosi">Inference in Bayesian Networks</A>, a tutorial by Bruce <B>D'Ambrosio</B> and Bob <B>Fung</B> is available through Bruce's home page, given at the Summer Institute on AI and Probability, 1994 
   * <A HREF="http://www.cs.cmu.edu/afs/cs/user/fgcozman/www/QuasiBayesianInformation/quasi-bayesian-translated/quasi-bayesian-translated.html"> An Introduction to Quasi-Bayesian Theory, Lower Probability, Choquet Capacities and Related Models</A>, by Fabio <B>Cozman</B>. 
   *  _Decision Theoretic Planning and Markov Decision Processes_ available under Introductory Material through <A TARGET="_blank" HREF="http://www.cs.brown.edu/people/tld/">Tom <B>Dean's</B> Homepage</A>. See also <A TARGET="_blank" HREF="http://www.cs.brown.edu/research/ai/dynamics/tutorial/home.html"> Learning Dynamical Systems: A Tutorial</A>. 
   *  <a TARGET="_blank" HREF="http://www.cs.cornell.edu/home/halpern/papers/iccs.ps">Reasoning about Uncertainty: A Logical Approach</a>, a tutorial paper from form</a>, from <a TARGET="_blank" HREF="http://www.cs.cornell.edu/home/halpern">Joe *Halpern's* homepage</a>. 
   *  <A TARGET="_blank" HREF="ftp://ftp.research.microsoft.com/pub/tech-reports/winter94-95/tr-95-06.ps">A Tutorial on Learning Bayesian Networks</A> and other articles from <A TARGET="_blank" HREF="http://research.microsoft.com/%7Eheckerman/""> David <B>Heckerman's</B> Homepage</A> 
   *  <A TARGET="_blank" HREF="http://www.research.microsoft.com/research/nips95bn/"> NIPS 95 Workshop on Learning in Bayesian Networks and Other Graphical Models</A> held in Vail, Colorado, Dec. 1995. Web page maintained by David Heckerman, Master of Ceremonies. 
   * A classic tutorial on <a TARGET="_blank" HREF="http://research.microsoft.com/~horvitz/dt.htm"> "Decision Theory in Expert Systems and Artificial Intelligence"</a> by Eric Horvitz, Jack Breese, and Max Henrion. Among other resources and pointers on <a TARGET="_blank" HREF="http://research.microsoft.com/~horvitz/">Eric <B>Horvitz</B>'s </a> home page. 
   *  <A TARGET="_blank" HREF="http://research.microsoft.com/~horvitz/CACM.htm"> Special Issue on Uncertainty in AI</A>, _Communications of the ACM_, March 1995- Volume 38, Number 3. 
   *  <A TARGET="_blank" HREF="http://bayes.wustl.edu/"> Probability Theory As Extended Logic</A> about the theories of E.T. <B>Jaynes</B>. A book under development by Jaynes, the Bayesian physisist and advocate of the Maximum Entropy principle, with some links to readings available online. 
   *  <A TARGET="_blank" HREF="http://www.cs.berkeley.edu/~jordan/papers/uai.ps.Z">Why the logistic function?</A> by M. <B>Jordan</B>. A tutorial discussing probabilistic approaches to neural networks, and their relationship with others. Other tutorials and papers by Jordan are in the <A TARGET="_blank" HREF="http://www.cs.berkeley.edu/~jordan/papers/"> same directory</A>. 
   *  <A TARGET="_blank" HREF="http://www.stat.washington.edu:80/tech.reports/tr254.ps"> Bayes Factors and Model Uncertainty</A>, by Robert <B>Kass</B> and Adrian <B>Raftery</B>, to appear in JASA. 
   *  <A TARGET="_blank" HREF="http://ite.gmu.edu/%7Eklaskey/modelco/index.htm"> Model Confidence</A> by Kathryn Blackmond <B>Laskey</B>. Postscript and HTML for an introduction to perspectives on how to treat confidence in models. 
   *  <A TARGET="_blank" HREF="http://www.astro.cornell.edu/staff/loredo/bayes/tjl.html"> Bayesian Inference in Astrophysics</A> by Tom <B>Loredo</B>. Tutorial introduction to Bayesian statistics. 
   *  <A TARGET="_blank" HREF="http://131.111.48.24/pub/mackay/info-theory/course.html"> A Short Course in Information Theory</A>, 8 lectures by David <B>MacKay</B>, HTML linked to Postscript. 
   *  <A TARGET="_blank" HREF="dnai_bda.ps.Z">Decision Analytic Networks in Artificial Intelligence</A> by <B>Matzkevich</B> and <B>Abramson</B> in the January 1995 issue of Management Science (41(1):1-22) 
   *  <A TARGET="_blank" HREF="http://www.cs.utoronto.ca/~radford/review.abstract.html"> Probabilistic Inference using Markov Chain Monte Carlo Methods</A> by Radford <B>Neal</B> 
   *  <I>An Introduction to Minimum Message Length Inference</I>, and other tutorials comparing MML, MDL and Bayesian inference at <A TARGET="_blank" HREF="http://www.cs.monash.edu.au/~lloyd/tildeMML/index.html"> Minimum Message Length Encoding </A> page 
   *  <A TARGET="_blank" HREF="ftp://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.ps.Z">Causal diagrams for empirical research</A>, <A TARGET="_blank" HREF="ftp://ftp.cs.ucla.edu/pub/stat_ser/R223-U.ps.Z">Causation, action, and counterfactuals</A>, <A TARGET="_blank" HREF="ftp://ftp.cs.ucla.edu/pub/stat_ser/R195-LLL.ps.Z">From Bayesian Networks to Causal Networks</A> and other articles in the <A TARGET="_blank" HREF="ftp://ftp.cs.ucla.edu/pub/stat_ser/INDEX.html">UCLA Statistical Series</A>, i.e., Technical Reports by <B>Pearl</B> and others. 
   *  <A TARGET="_blank" HREF="http://www.rpal.rockwell.com:80/~peot/dxtalk.ps"> Tutorial on Diagnosis</A> by Mark <B>Peot</B> and Greg <B>Provan</B>, given at the Summer Institute on AI and Probability, 1994. 
   *  <A TARGET="_blank" HREF="http://www.stat.washington.edu:80/tech.reports/bic.ps"> Bayesian Model Selection in Sociology</A>, by Adrian <B>Raftery</B> (with Discussion by Andrew Gelman &amp; Donald Rubin, and Robert Hauser, and a Rejoinder) 
   *  <A TARGET="_blank" HREF="ftp://aig.jpl.nasa.gov/pub/smyth/papers/TR-96-03.ps.Z"> Probabilistic Independence Networks for Hidden Markov Models</A>, by Padhraic <B>Smyth</B>, David Heckerman, and Michael Jordan. Tech. Report from 3 institutions. 
   *  <A TARGET="_blank" HREF="http://ai.eecs.umich.edu/people/wellman/tut/Abstraction.html"> Abstraction in Probabilistic Reasoning</A> by Michael <B>Wellman</B>, given at the Summer Institute on AI and Probability, 1994 </UL> 

#InfoServ
---++ Related Information Servers</a> 
   *  [[http://excalibur.brc.uconn.edu" target="_blank][BayesNet pages at Air Force Inst. of Technology]] 
   *  [[http://www.rahul.net/lumina/DA.html" target="_blank][Decision/Risk Analysis]] 
   *  <A HREF="http://omega.albany.edu:8008/isba/" target="_blank">International Society for Bayesian Analysis</A> 
   *  <A HREF="http://info.gte.com/~kdd/" target="_blank"> Knowledge Discovery Mine</A> for information on knowledge discovery. 
   *  <A HREF="http://www.ai.univie.ac.at/oefai/ml/ml-ressources.html" target="_blank"> Machine Learning Information Services</A> 
   * <A HREF="http://www.cs.monash.edu.au/~lloyd/tildeMML/index.html" target="_blank"> Minimum Message Length Encoding</A> 
   * <A HREF="http://www.ph.tn.tudelft.nl/PRInfo.html" target="_blank">Pattern Recognition Information</A> 
   *  <A HREF="http://bayes.stat.washington.edu/almond/belief.html" target="_blank">Software for Belief Networks</A> maintained by Russell Almond. <LI> <A HREF="http://www.research.microsoft.com/research/dtg/acm.html" target="_blank">Special issue on Real-World Applications of Uncertain Reasoning</A>, <I>Communications of the ACM</I>, March 1995, based in part on UAI-93. 
   *  <A HREF="http://www.vuse.vanderbilt.edu/~dfisher/ai-stats/society.html" target="_blank"> The Society For Artificial Intelligence and Statistics</a> 
   *  <A HREF="http://sigart.acm.org/">SIGART Electronic Information Service</A> 
   *  [[http://www.isds.duke.edu/stats.html" target="_blank][Statistics servers]] maintained at [[http://www.isds.duke.edu/" target="_blank][Institute of Statistics and Decision Sciences, Duke Univ]] 
   *  <A HREF="ftp://lib.stat.cmu.edu/.index.html" target="_blank">Statlib Index</A> a system for distributing statistical software, datasets, and information maintained at CMU. 
   *  <A HREF="http://www.dia.uned.es/~fjdiez/lerpia.html" target="_blank">LERPIA</A> (Electronic List on Probabilistic Reasoning in Artificial Intelligence) to coordinate the Spanish researchers with special interest on Bayesian networks. <LI> <A HREF="http://www.isds.duke.edu/sbss/sbss.html" target="_blank"> ASA Section on Bayesian Statistical Sciences</A> <LI> <A HREF="http://www.fuqua.duke.edu/faculty/daweb/" target="_blank"> Decision Analysis Society of INFORMS.</a> </UL></td> </tr> </table>
to top
End of topic
Skip to action links | Back to top
You are here: Main > BenefitArchival

to top