Multi-partition time aggregation for Markov Chains
Multi-partition time aggregation for Markov Chains
Motivated by Markov decision processes, this paper introduces a form of embedding for Markov chains which is based on the partition of the state space into a manageable number of subsets, with the aim of enabling a decomposition algorithm for calculating long-term costs and probabilities. The decomposition enables the decision maker to derive the long term distribution by making use of evaluations in the domain of the partitions, which presents reduced cardinality with respect to the original state space and hence yields reduced computational effort.
4922-4927
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Fragoso, Marcelo D.
7f484139-de97-4458-aa6b-dc3249811a08
Ourique, Fabricio O.
c2b933e0-dd92-4260-83f2-c3982f4911e9
18 January 2018
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Fragoso, Marcelo D.
7f484139-de97-4458-aa6b-dc3249811a08
Ourique, Fabricio O.
c2b933e0-dd92-4260-83f2-c3982f4911e9
Arruda, Edilson F., Fragoso, Marcelo D. and Ourique, Fabricio O.
(2018)
Multi-partition time aggregation for Markov Chains.
In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017.
vol. 2018-January,
IEEE.
.
(doi:10.1109/CDC.2017.8264387).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Motivated by Markov decision processes, this paper introduces a form of embedding for Markov chains which is based on the partition of the state space into a manageable number of subsets, with the aim of enabling a decomposition algorithm for calculating long-term costs and probabilities. The decomposition enables the decision maker to derive the long term distribution by making use of evaluations in the domain of the partitions, which presents reduced cardinality with respect to the original state space and hence yields reduced computational effort.
This record has no associated files available for download.
More information
Published date: 18 January 2018
Venue - Dates:
56th IEEE Annual Conference on Decision and Control, CDC 2017, , Melbourne, Australia, 2017-12-12 - 2017-12-15
Identifiers
Local EPrints ID: 446135
URI: http://eprints.soton.ac.uk/id/eprint/446135
PURE UUID: 80c157ce-bfda-4d32-85d8-5ea559fd862d
Catalogue record
Date deposited: 21 Jan 2021 17:35
Last modified: 18 Mar 2024 03:59
Export record
Altmetrics
Contributors
Author:
Edilson F. Arruda
Author:
Marcelo D. Fragoso
Author:
Fabricio O. Ourique
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics