Year: 2005
Type: dissertation
Abstract: Graphical models have been widely used in many applications, ranging from human behavior recognition to wireless signal detection. However, efficient inference and learning techniques for graphical models are needed to handle complex models, such as hybrid Bayesian networks. This thesis proposes extensions of expectation propagation, a powerful generalization of loopy belief propagation, to develop efficient inference and learning algorithms for graphical models. The first two chapters of the thesis present inference algorithms for generative graphical models, and the next two propose learning algorithms for conditional graphical models. First, the thesis proposes a window-based EP smoothing algorithm, as an alternative to batch EP, for hybrid dynamic Bayesian networks. For an application to digital wireless communications, window-based EP smoothing achieves estimation accuracy comparable to sequential Monte Carlo methods, but with more than 10 times less computational cost. Second, it combines tree-structured EP approximation with the junction tree algorithm
Authors: Yuan Qi, Rosalind W. Picard
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1520194733