Approximate Counting of Graphical Models via MCMC Revisited.
Dag SonntagJosé M. PeñaManuel Gómez-OlmedoPublished in: Int. J. Intell. Syst. (2015)
Keyphrases
- graphical models
- approximate inference
- belief propagation
- probabilistic model
- markov chain monte carlo
- random variables
- probabilistic inference
- probabilistic graphical models
- structure learning
- bayesian networks
- conditional random fields
- belief networks
- markov networks
- conditional independence
- generative model
- factor graphs
- monte carlo
- exact inference
- markov chain
- nonparametric belief propagation
- variational methods
- message passing
- dynamic bayesian networks
- importance sampling
- structural learning
- graphical structure
- map inference
- undirected graphical models
- loopy belief propagation
- particle filtering
- parameter estimation
- posterior distribution