Prune Sampling: a MCMC inference technique for discrete and deterministic Bayesian networks.
Frank PhillipsonJurriaan ParieRon WeikampPublished in: CoRR (2019)
Keyphrases
- markov chain monte carlo
- bayesian networks
- posterior probability
- approximate inference
- monte carlo
- sampling algorithm
- posterior distribution
- markov chain
- exact inference
- probabilistic inference
- continuous variables
- metropolis hastings
- gibbs sampling
- inference in bayesian networks
- importance sampling
- parameter estimation
- graphical models
- generative model
- markov chain monte carlo sampling
- gibbs sampler
- bayesian inference
- probability distribution
- probabilistic reasoning
- dynamic bayesian networks
- conditional independence
- discrete variables
- probabilistic model
- parameter learning
- structure learning
- dirichlet process
- search space
- particle filter
- conditional probabilities
- hidden variables
- multiply sectioned bayesian networks
- metropolis hastings algorithm
- bayesian network inference
- random variables
- random sampling
- belief propagation
- variable elimination
- probabilistic modeling
- data association
- bayesian framework
- band limited
- markov blanket
- em algorithm
- expectation maximization
- credal networks
- gaussian process