This lecture covers the motivation behind the Manbou Chain and Monte Carlo methods, aiming to find the maximum of a function. The instructor explains the goal, challenges, and the process of building an invariant distribution for the Monte Carlo chain.
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This course focuses on dynamic models of random phenomena, and in particular, the most popular classes of such models: Markov chains and Markov decision processes. We will also study applications in q
Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.
