Output-Weighted Importance Sampling for Bayesian Experimental Design and Uncertainty Quantification.
Antoine BlanchardThemistoklis P. SapsisPublished in: CoRR (2020)
Keyphrases
- experimental design
- importance sampling
- monte carlo
- posterior distribution
- markov chain monte carlo
- empirical studies
- active learning
- markov chain
- kalman filter
- particle filter
- approximate inference
- particle filtering
- bayesian networks
- sample size
- bayesian inference
- probability distribution
- conditional probabilities
- upper bound
- latent variables
- feature selection
- parameter estimation
- maximum likelihood
- bayesian framework
- virtual learning environments
- graph cuts