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Lecture

Geometric Ergodicity: Convergence Diagnostics

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MATH-414: Stochastic simulation

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 sampling methods as Monte Carlo, quasi Monte Carlo, Mark ...

Description

This lecture covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains. The instructor explains the importance of density functions and the process of evaluating them. Various properties of the chain are discussed, such as bias and relaxation time estimation.

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Instructor

Fabio Nobile

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