Discrete Samplers for Approximate Inference in Probabilistic Machine Learning.
Shirui ZhaoNimish ShahWannes MeertMarian VerhelstPublished in: DATE (2022)
Keyphrases
- approximate inference
- machine learning
- graphical models
- bayesian networks
- markov chain monte carlo
- factor graphs
- probabilistic model
- belief propagation
- exact inference
- parameter estimation
- probabilistic inference
- gaussian process
- latent variables
- message passing
- conditional random fields
- loopy belief propagation
- belief networks
- dynamic bayesian networks
- bayesian inference
- gibbs sampling
- expectation propagation
- conditional probabilities
- discrete variables
- generative model
- posterior probability
- random variables
- learning algorithm
- model selection
- continuous variables
- information extraction
- markov random field
- probability distribution
- structured prediction
- free energy
- bayesian framework
- feature selection
- clustering algorithm
- active learning
- hidden markov models
- dynamic programming
- supervised learning
- distributed systems
- maximum likelihood
- graph cuts