Markov Chains Derived From Lagrangian Mechanical Systems

The theory of Markov chains with countable state spaces is a greatly developed and successful area of probability theory and statistics. There is much interest in continuing to develop the theory of Markov chains beyond countable state spaces. One needs good and well motivated model systems in this effort. In this thesis, we propose to produce such systems by introducing randomness into familiar deterministic systems so that we can draw upon the existing: deterministic) results to aid the analysis of our Markov chains. We will focus most heavily on models drawn from Lagrangian mechanical systems with collisions: billiards).

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mathematics

Year dc:date.available

2011

Author dc:creator

• Cook, Scott

Contributors dc:contributor

• Renato Feres

• Mathematics
• Mechanics
• dynamical systems
• Markov chains
• probabilty

Language dc:language

English (en)

DOI

https://doi.org/10.7936/K7JH3J95

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:etd-1074