Sandpiper: Scaling probabilistic inferencing to large scale graphical models.
Alexander UlanovManish MarwahMijung KimRoshan DathathriCarlos ZubietaJun LiPublished in: IEEE BigData (2017)
Keyphrases
- graphical models
- probabilistic model
- bayesian networks
- belief networks
- belief propagation
- factor graphs
- random variables
- probabilistic inference
- generative model
- probabilistic graphical models
- probabilistic networks
- map inference
- conditional random fields
- approximate inference
- conditional independence
- markov networks
- structure learning
- probabilistic reasoning
- exact inference
- statistical inference
- gaussian graphical models
- relational models
- directed graphical models
- nonparametric belief propagation
- graphical structure
- knowledge representation
- statistical relational learning
- loopy belief propagation
- probabilistic logic
- probability distribution
- posterior probability
- message passing
- bayesian belief networks
- markov logic networks
- latent variables
- bayesian inference
- structural learning
- directed acyclic
- probability theory
- lower bound
- search algorithm