Approximating Posterior Distributions in Belief Networks Using Mixtures.
Christopher M. BishopNeil D. LawrenceTommi S. JaakkolaMichael I. JordanPublished in: NIPS (1997)
Keyphrases
- belief networks
- posterior distribution
- probability distribution
- expectation maximization
- conditional probabilities
- probabilistic inference
- markov chain monte carlo
- mixture model
- posterior probability
- latent variables
- parameter estimation
- graphical models
- bayesian framework
- maximum a posteriori
- random variables
- exact inference
- bayesian networks
- probabilistic model
- probability density function
- hyperparameters
- image processing
- least squares
- image segmentation
- computer vision
- belief propagation
- gaussian mixture model
- image reconstruction
- model selection
- text classification
- gaussian distribution
- supervised learning
- gaussian process