Approximate inference for the loss-calibrated Bayesian.
Simon Lacoste-JulienFerenc HuszarZoubin GhahramaniPublished in: AISTATS (2011)
Keyphrases
- approximate inference
- bayesian networks
- graphical models
- probabilistic inference
- variational approximation
- expectation propagation
- markov chain monte carlo
- parameter estimation
- belief propagation
- message passing
- exact inference
- variational methods
- gaussian process
- conditional random fields
- posterior distribution
- latent variables
- dynamic bayesian networks
- variational inference
- factor graphs
- loopy belief propagation
- maximum likelihood
- bayesian inference
- posterior probability
- probabilistic model
- bayesian learning
- multi view
- markov random field
- structured prediction
- free energy
- computer vision
- least squares
- machine learning
- belief networks
- conditional probabilities
- image segmentation
- generative model