Bayesian structure learning using dynamic programming and MCMC.
Daniel EatonKevin P. MurphyPublished in: UAI (2007)
Keyphrases
- structure learning
- bayesian networks
- dynamic programming
- posterior distribution
- posterior probability
- parameter estimation
- markov chain monte carlo
- graphical models
- approximate inference
- markov networks
- bayesian inference
- parameter learning
- probability distribution
- markov chain
- conditional independence
- maximum likelihood
- bayesian network structures
- transfer learning
- bayesian network classifiers
- probabilistic model
- markov logic networks
- probabilistic inference
- markov blanket
- conditional probabilities
- state space
- probabilistic graphical models
- latent variables
- generative model
- model selection
- sample size
- machine learning
- reinforcement learning
- least squares
- bayesian framework
- prior information
- network structure
- data points
- em algorithm
- probabilistic reasoning
- hidden markov models
- particle filtering
- probability density function
- random variables