MAGIC093: Markov Processes

Course details

A core MAGIC course

Semester

Spring 2022
Monday, January 31st to Friday, March 25th; Monday, April 25th to Friday, May 6th

Hours

Live lecture hours
20
Recorded lecture hours
0
Total advised study hours
80

Timetable

Wednesdays
11:05 - 11:55
Thursdays
12:05 - 12:55

Course forum

Visit the MAGIC093 forum

Description

Markov processes in discrete and continuous time will be presented for finite and countable state spaces in discrete time, as well as for some basic processes in continuous time. Standard material will include generators, Dynkinâ's formula, ergodicity, and (strong) Markov and (strong) Feller properties. A bit more advanced material could include coupling and recurrence applied to convergence rates. Some attention will also be paid to the applicability of Markov processes in a variety of fields such as economics, operational research, biology, and physics. Time permitting we will also look at Monte Carlo Markov chain (MCMC) methods as used in, e.g., (Bayesian) statistics. 

Prerequisites

Some basic knowledge about Markov chains is highly desirable. 

Syllabus

1. Stochastic processes. Definitions of a Markov process. 2. Examples: Random Walks. Generators. Chapman-Kolmogorov equations. 3. Dynkin's identity. Stopping times, (strong) Markov property, (strong) Feller processes. 4. Irreducible Markov processes, ergodic theorems, conditions of ergodicity. 5. Coupling method and application to convergence rates. 6. Applications and MCMC.

Lecturer

  • JT

    Professor Jacco Thijssen

    University
    University of York

Bibliography

No bibliography has been specified for this course.

Assessment

Attention needed

Assessment information will be available nearer the time.

Files

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Lectures

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