Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.
Explores the quasi-stationary distribution approach in molecular dynamics modeling, covering Langevin dynamics, metastability, and kinetic Monte Carlo models.
Explores unstructured and structured search and routing protocols, emphasizing the importance of network structure assumptions and introducing the 'Bubble Storm' algorithm.
Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.
