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Multi-partition time aggregation for Markov Chains

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
Institute of Electrical and Electronics Engineers Inc.
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.
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, Institute of Electrical and Electronics Engineers Inc. pp. 4922-4927 . (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.

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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
ORCID for Edilson F. Arruda: ORCID iD orcid.org/0000-0002-9835-352X

Catalogue record

Date deposited: 21 Jan 2021 17:35
Last modified: 28 Apr 2022 02:31

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Contributors

Author: Marcelo D. Fragoso
Author: Fabricio O. Ourique

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