Quasi-Stationary Distribution: Molecular Dynamics Modeling

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.

Explores the early days of computer simulation, focusing on the Monte Carlo method and its evolution in scientific research.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.