A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems.
Sijing LiCheng ZhangZhiwen ZhangHongkai ZhaoPublished in: Stat. Comput. (2023)
Keyphrases
- data driven
- inverse problems
- monte carlo method
- markov chain
- posterior distribution
- bayesian learning
- image reconstruction
- partial differential equations
- global optimization
- monte carlo
- convex optimization
- optimization methods
- genetic algorithm
- early vision
- optimization problems
- maximum a posteriori
- latent variables
- probability distribution
- bayesian networks
- bayesian framework
- posterior probability
- reinforcement learning
- image processing
- learning machines
- support vector
- mathematical models
- bayesian inference
- higher order
- parameter estimation