MATH-414: Stochastic simulation

Lectures in this course (48)

Covers the Metropolis-Hastings algorithm for stochastic simulation and discusses its steps and convergence.

Covers the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for generating samples from a target probability distribution.

Covers the Metropolis-Hastings algorithm and convergence diagnostics in stochastic simulation, focusing on sampling and proposal generation.

Introduces Markov chains and Metropolis Hastings algorithm in stochastic simulation.

Explores geometric ergodicity in Markov chains and estimators' bias and variance, highlighting efficiency loss quantification.

Covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains.

Covers the estimation of relaxation time in chains and the importance of sample sizes.

Explores stochastic simulation, rare events, and the Crude Monte Carlo method, emphasizing the importance of thresholds and closed-form expressions.

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