Approximation of Markov Processes by Lower Dimensional Processes

Ioannis Tzortzis*, Charalambos D. Charalambous, Themistoklis Charalambous, Christoforos N. Hadjicostis, Mikael Johansson

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

In this paper, we investigate the problem of aggregating a given finite-state Markov process by another process with fewer states. The aggregation utilizes total variation distance as a measure of discriminating the Markov process by the aggregate process, and aims to maximize the entropy of the aggregate process invariant probability, subject to a fidelity described by the total variation distance ball. An iterative algorithm is presented to compute the invariant distribution of the aggregate process, as a function of the invariant distribution of the Markov process. It turns out that the approximation method via aggregation leads to an optimal aggregate process which is a hidden Markov process, and the optimal solution exhibits a water-filling behavior. Finally, the algorithm is applied to specific examples to illustrate the methodology and properties of the approximations.

AlkuperäiskieliEnglanti
Otsikko2014 IEEE 53rd Annual Conference on Decision and Control (CDC)
KustantajaIEEE
Sivut4441-4446
Sivumäärä6
ISBN (elektroninen)978-1-4673-6090-6
DOI - pysyväislinkit
TilaJulkaistu - 2014
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Conference on Decision and Control - Los Angeles, Kanada
Kesto: 15 joulukuuta 201417 joulukuuta 2014
Konferenssinumero: 53

Conference

ConferenceIEEE Conference on Decision and Control
LyhennettäCDC
MaaKanada
KaupunkiLos Angeles
Ajanjakso15/12/201417/12/2014

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