Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.
Explores importance sampling through a change of variable to speed up Monte Carlo calculations and discusses the impact on stochastic estimates and variance scaling.
Explores Monte Carlo techniques for sampling and simulation, covering integration, importance sampling, ergodicity, equilibration, and Metropolis acceptance.
Explores the quasi-stationary distribution approach in molecular dynamics modeling, covering Langevin dynamics, metastability, and kinetic Monte Carlo models.
