Stochastic Simulation: Markov Chains and Transition Matrices

This lecture covers the concept of Markov chains and transition matrices in the context of stochastic simulation. It explains the difficulties in modeling high-dimensional spaces and provides examples from statistical physics. The instructor discusses the energy function, Bayesian statistics, and the Markov property. The lecture delves into the construction of Markov chains, the estimation of energy functions, and the properties of transition matrices. It also explores the invariant vectors of transition matrices and their significance in stochastic processes.