Creating non-minimal triangulations for use in inference in mixed stochastic/deterministic graphical models.
Chris D. BartelsJeff A. BilmesPublished in: Mach. Learn. (2011)
Keyphrases
- graphical models
- probabilistic inference
- exact inference
- map inference
- belief propagation
- belief networks
- bayesian networks
- factor graphs
- undirected graphical models
- loopy belief propagation
- efficient inference algorithms
- approximate inference
- probabilistic graphical models
- nonparametric belief propagation
- random variables
- statistical inference
- probabilistic model
- directed graphical models
- structure learning
- conditional random fields
- graphical structure
- probabilistic reasoning
- possibilistic networks
- probabilistic networks
- statistical relational learning
- markov networks
- collective classification
- markov logic networks
- relational dependency networks
- message passing
- bayesian inference
- conditional independence
- random fields
- free energy
- exponential family
- influence diagrams
- markov random field
- parameter learning
- dynamic bayesian networks
- image labeling
- structured prediction
- generative model