Exploring Graphical Models with Bayesian Learning and MCMC for Failure Diagnosis.
Hongfei WangWenjie CaiJianwen LiKun HePublished in: ASP-DAC (2020)
Keyphrases
- bayesian learning
- graphical models
- failure diagnosis
- posterior distribution
- approximate inference
- latent variables
- markov chain monte carlo
- random variables
- probabilistic model
- discrete event systems
- failure detection
- belief propagation
- fault diagnosis
- model selection
- probabilistic inference
- probability distribution
- probabilistic graphical models
- bayesian networks
- parameter estimation
- model based diagnosis
- bayesian framework
- conditional random fields
- structure learning
- posterior probability
- markov networks
- hyperparameters
- exact inference
- belief networks
- monte carlo
- maximum a posteriori
- markov chain
- variational methods
- expectation maximization
- gaussian distribution
- message passing
- exponential family
- incremental learning
- latent variable models
- bayesian inference
- generative model
- em algorithm
- maximum likelihood
- expert systems