Learning discrete decomposable graphical models via constraint optimization.
Tomi JanhunenMartin GebserJussi RintanenHenrik J. NymanJohan PensarJukka CoranderPublished in: Stat. Comput. (2017)
Keyphrases
- graphical models
- markov networks
- constraint optimization
- belief propagation
- structural learning
- random variables
- approximate inference
- probabilistic model
- probabilistic graphical models
- learning algorithm
- probabilistic inference
- structured prediction
- upper bound
- linear programming
- conditional random fields
- markov logic networks
- reinforcement learning