Bayesian structure learning using dynamic programming and MCMC
Daniel EatonKevin P. MurphyPublished in: CoRR (2012)
Keyphrases
- structure learning
- bayesian networks
- dynamic programming
- posterior probability
- markov chain monte carlo
- posterior distribution
- parameter estimation
- graphical models
- approximate inference
- markov networks
- bayesian inference
- conditional independence
- parameter learning
- markov chain
- markov logic networks
- probability distribution
- maximum likelihood
- transfer learning
- probabilistic inference
- bayesian network structures
- probabilistic model
- bayesian network classifiers
- latent variables
- random variables
- bayesian framework
- stereo matching
- maximum likelihood estimation
- conditional probabilities
- state space
- probabilistic reasoning
- particle filtering
- markov blanket
- em algorithm
- labeled data
- prior knowledge
- markov random field
- probabilistic graphical models
- model selection
- gene regulatory networks
- text categorization
- network structure
- probability density function