Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes.
David J. WarneThomas P. PrescottRuth E. BakerMatthew J. SimpsonPublished in: J. Comput. Phys. (2022)
Keyphrases
- monte carlo
- stochastic processes
- partially observed
- random fields
- markov chain monte carlo
- dynamic bayesian networks
- stochastic process
- bayesian networks
- markov chain
- monte carlo methods
- probability distribution
- bayesian inference
- policy evaluation
- importance sampling
- maximum entropy
- monte carlo simulation
- particle filter
- continuous time bayesian networks
- random variables
- posterior distribution
- monte carlo tree search
- variational inference
- particle filtering
- variational approximation
- conditional random fields
- exact inference
- non stationary
- matrix inversion
- markov random field
- posterior probability
- belief networks
- probabilistic inference
- approximate inference
- bayesian learning
- finite state