Stochastic Simulation: Markov Chains and Metropolis Hastings
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Description
This lecture covers the concepts of Markov chains in several state spaces and introduces the Metropolis Hastings algorithm through examples. It also discusses the concept of densities and measures in the context of stochastic simulation.
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The student who follows this course will get acquainted with computational tools used to analyze systems with uncertainty arising in engineering, physics, chemistry, and economics. Focus will be on s
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
