Regression-based Bayesian estimation and structure learning for nonparanormal graphical models.
Jami J. MulgraveSubhashis GhosalPublished in: Stat. Anal. Data Min. (2022)
Keyphrases
- bayesian estimation
- structure learning
- graphical models
- random variables
- bayesian networks
- belief propagation
- parameter learning
- probabilistic model
- probabilistic graphical models
- markov networks
- conditional independence
- markov logic networks
- approximate inference
- exact inference
- posterior distribution
- hidden variables
- conditional random fields
- undirected graphical models
- factor graphs
- statistical relational learning
- gaussian process
- machine learning
- pairwise