Posterior Variance Predictions in Sparse Bayesian Learning under Approximate Inference Techniques.
Christo Kurisummoottil ThomasDirk T. M. SlockPublished in: ACSSC (2020)
Keyphrases
- approximate inference
- sparse bayesian learning
- gaussian process
- markov chain monte carlo
- graphical models
- parameter estimation
- probabilistic inference
- exact inference
- posterior distribution
- variational methods
- belief propagation
- latent variables
- gaussian processes
- probabilistic model
- dynamic bayesian networks
- model selection
- bayesian networks
- message passing
- probabilistic graphical models
- importance sampling
- bayesian framework
- hyperparameters
- factor graphs
- loopy belief propagation
- regression model
- posterior probability
- probability distribution
- cross validation
- partition function
- supervised learning
- markov random field
- structured prediction
- upper bound
- semi supervised