The Thirteenth UAI Bayesian

Stochastic dynamic programming with factored representations

Markov decision processes (MDPs) have proven to be popular models for decision-theoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, state-based specifications and computations. To alleviate the combinatorial problems associated with such methods, we propose new representational and computational techniques for MDPs that exploit certain types of prob...

Combinatonal Optimization by Learning and Simulation of Bayesian Networks

Bayesian Control for Concentrating Mixed Nuclear Waste

A control algorithm for batch processing of mixed waste is proposed based on conditional Gaussian Bayesian networks. The network is compiled during batch staging for real-time re­ sponse to sensor input.

Bayesian Model Averaging Using the k-best Bayesian Network Structures

We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks. We present empirical results on structural discovery over several real and synthetic data sets and show that t...

UAI06 Inference Evaluation Results

This brief report describes the results of the UAI 2006 inference evaluation. The web page with more information and the complete Bayesian network listing is at http://ssli.ee.washington.edu/ ̃bilmes/ uai06InferenceEvaluation/results.