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Old Resources List
AUAI Tutorials and Survey Sources
Tutorials are nominated by present and former members of the UAI program committee. Listed by first author.
- Inference in Bayesian Networks, a tutorial by Bruce D'Ambrosio and Bob Fung is available through Bruce's home page, given at the Summer Institute on AI and Probability, 1994
- An Introduction to Quasi-Bayesian Theory, Lower Probability, Choquet Capacities and Related Models, by Fabio Cozman.
- Decision Theoretic Planning and Markov Decision Processes available under Introductory Material through Tom Dean's Homepage. See also Learning Dynamical Systems: A Tutorial.
- Reasoning about Uncertainty: A Logical Approach, a tutorial paper from form, from Joe Halpern's homepage.
- A Tutorial on Learning Bayesian Networks and other articles from David Heckerman's Homepage
- NIPS 95 Workshop on Learning in Bayesian Networks and Other Graphical Models held in Vail, Colorado, Dec. 1995. Web page maintained by David Heckerman, Master of Ceremonies.
- A classic tutorial on "Decision Theory in Expert Systems and Artificial Intelligence" by Eric Horvitz, Jack Breese, and Max Henrion. Among other resources and pointers on Eric Horvitz's home page.
- Special Issue on Uncertainty in AI, Communications of the ACM, March 1995- Volume 38, Number 3.
- Probability Theory As Extended Logic about the theories of E.T. Jaynes. A book under development by Jaynes, the Bayesian physisist and advocate of the Maximum Entropy principle, with some links to readings available online.
- Why the logistic function? by M. Jordan. A tutorial discussing probabilistic approaches to neural networks, and their relationship with others. Other tutorials and papers by Jordan are in the same directory.
- Bayes Factors and Model Uncertainty, by Robert Kass and Adrian Raftery, to appear in JASA.
- Model Confidence by Kathryn Blackmond Laskey. Postscript and HTML for an introduction to perspectives on how to treat confidence in models.
- Bayesian Inference in Astrophysics by Tom Loredo. Tutorial introduction to Bayesian statistics.
- A Short Course in Information Theory, 8 lectures by David MacKay, HTML linked to Postscript.
- Decision Analytic Networks in Artificial Intelligence by Matzkevich and Abramson in the January 1995 issue of Management Science (41(1):1-22)
- Probabilistic Inference using Markov Chain Monte Carlo Methods by Radford Neal
- An Introduction to Minimum Message Length Inference, and other tutorials comparing MML, MDL and Bayesian inference at Minimum Message Length Encoding page
- Causal diagrams for empirical research, Causation, action, and counterfactuals, From Bayesian Networks to Causal Networks and other articles in the UCLA Statistical Series, i.e., Technical Reports by Pearl and others.
- Tutorial on Diagnosis by Mark Peot and Greg Provan, given at the Summer Institute on AI and Probability, 1994.
- Bayesian Model Selection in Sociology, by Adrian Raftery (with Discussion by Andrew Gelman & Donald Rubin, and Robert Hauser, and a Rejoinder)
- Probabilistic Independence Networks for Hidden Markov Models, by Padhraic Smyth, David Heckerman, and Michael Jordan. Tech. Report from 3 institutions.
- Abstraction in Probabilistic Reasoning by Michael Wellman, given at the Summer Institute on AI and Probability, 1994
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