Causal Graphical Models with Latent Variables: Learning and Inference.
Stijn MeganckPhilippe LerayBernard ManderickPublished in: ECSQARU (2007)
Keyphrases
- graphical models
- latent variables
- factor graphs
- probabilistic model
- approximate inference
- exact inference
- bayesian networks
- structured prediction
- random variables
- probabilistic graphical models
- latent variable models
- probabilistic inference
- belief propagation
- hidden variables
- relational dependency networks
- markov networks
- markov logic networks
- parameter learning
- undirected graphical models
- dynamic bayesian networks
- belief networks
- prior knowledge
- max margin
- learning algorithm
- loopy belief propagation
- conditional random fields
- variational methods
- structure learning
- statistical relational learning
- topic models
- message passing
- bayesian inference
- map inference
- possibilistic networks
- efficient inference algorithms
- free energy
- expectation maximization
- conditional independence
- partition function
- posterior distribution