Approximating Markov Processes by Averaging
Abstract
Normally, one thinks of probabilistic transition systems as taking an initial probability distribution over the state space into a new probability distribution representing the system after a transition. We, however, take a dual view of Markov processes as transformers of bounded measurable functions. This is very much in the same spirit as a “predicate-transformer” view, which is dual to the state-transformer view of transition systems. We redevelop the theory of labelled Markov processes from this viewpoint; in particular, we explore approximation theory. We obtain three main results.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2014
- Source ID
- 10.1145/2537948
Entities
People
- Gordon Plotkin
- Philippe Chaput
- Prakash Panangaden
- Vincent Danos
Organizations
- McGill University
- Natural Sciences and Engineering Research Council
- Office of Naval Research
- University of Edinburgh