Inference Networks for Sequential Monte Carlo in Graphical Models.
Brooks PaigeFrank D. WoodPublished in: ICML (2016)
Keyphrases
- graphical models
- sequential monte carlo
- probabilistic inference
- probabilistic networks
- exact inference
- map inference
- belief networks
- bayesian networks
- belief propagation
- factor graphs
- loopy belief propagation
- approximate inference
- probabilistic model
- efficient inference algorithms
- random variables
- probabilistic graphical models
- undirected graphical models
- markov networks
- directed graphical models
- visual tracking
- structure learning
- possibilistic networks
- statistical relational learning
- probabilistic reasoning
- particle filter
- conditional random fields
- relational dependency networks
- importance sampling
- markov chain monte carlo
- inference process
- particle filtering
- parameter estimation
- markov logic networks
- markov chain
- markov random field
- computer vision
- higher order
- posterior distribution
- bayesian inference
- noise level
- message passing