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UAI
1998
13 years 11 months ago
Learning From What You Don't Observe
The process of diagnosis involves learning about the state of a system from various observations of symptoms or findings about the system. Sophisticated Bayesian (and other) algor...
Mark A. Peot, Ross D. Shachter
UAI
2000
13 years 11 months ago
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
UAI
2000
13 years 11 months ago
Utilities as Random Variables: Density Estimation and Structure Discovery
Decision theory does not traditionally include uncertainty over utility functions. We argue that the a person's utility value for a given outcome can be treated as we treat o...
Urszula Chajewska, Daphne Koller
UAI
2000
13 years 11 months ago
Minimum Message Length Clustering Using Gibbs Sampling
The K-Means and EM algorithms are popular in clustering and mixture modeling due to their simplicity and ease of implementation. However, they have several significant limitations...
Ian Davidson
UAI
2000
13 years 11 months ago
Building a Stochastic Dynamic Model of Application Use
Many intelligent user interfaces employ application and user models to determine the user's preferences, goals and likely future actions. Such models require application anal...
Peter Gorniak, David Poole