Adversarial α-divergence minimization for Bayesian approximate inference.
Simón Rodríguez SantanaDaniel Hernández-LobatoPublished in: Neurocomputing (2022)
Keyphrases
- approximate inference
- bayesian networks
- graphical models
- variational approximation
- expectation propagation
- probabilistic inference
- parameter estimation
- markov chain monte carlo
- exact inference
- belief propagation
- gaussian process
- variational methods
- factor graphs
- loopy belief propagation
- message passing
- posterior distribution
- latent variables
- posterior probability
- bayesian inference
- dynamic bayesian networks
- conditional random fields
- variational inference
- probability distribution
- random variables
- maximum likelihood
- probabilistic model
- three dimensional
- structured prediction
- kl divergence
- bayesian framework
- least squares
- markov random field
- hidden markov models
- machine learning
- kullback leibler
- support vector