This cluster of papers focuses on the application of Bayesian Monte Carlo methods, such as Markov Chain Monte Carlo (MCMC), Approximate Bayesian Computation, and Hamiltonian Monte Carlo, in scientific inference for inverse problems, model selection, and statistical estimation. It also explores adaptive MCMC algorithms and stochastic gradient Langevin dynamics for efficient parameter inference and approximation algorithms.
Bayesian Monte Carlo; Markov Chain; Approximate Bayesian Computation; Adaptive MCMC; Hamiltonian Monte Carlo; Stochastic Gradient Langevin Dynamics; Inverse Problems; Model Selection; Statistical Inference; Approximation Algorithms