### Simulation II: Markov Chains (Introduction to Statistical Computing)

Lecture
15: Combing multiple dependent random variables in a simulation; ordering
the simulation to do the easy parts first. Markov chains as a particular
example of doing the easy parts first. The Markov property. How to write a
Markov chain simulator. Verifying that the simulator works by looking at
conditional distributions. Variations on Markov
models: hidden Markov
models, interacting processes, continuous
time, chains with
complete connections. Asymptotics of Markov chains via linear algebra; the
law of large numbers (ergodic theorem) for Markov chains: we can approximate
expectations as soon as we can simulate.

*Readings*: Handouts on Markov chains and Monte Carlo

Introduction to Statistical Computing

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