Recursive estimation of high-order Markov chains: Approximation by finite mixtures
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00447119" target="_blank" >RIV/67985556:_____/16:00447119 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.ins.2015.07.038" target="_blank" >http://dx.doi.org/10.1016/j.ins.2015.07.038</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2015.07.038" target="_blank" >10.1016/j.ins.2015.07.038</a>
Alternative languages
Result language
angličtina
Original language name
Recursive estimation of high-order Markov chains: Approximation by finite mixtures
Original language description
A high-order Markov chain is a universal model of stochastic relations between discrete-valued variables. The exact estimation of its transition probabilities suffers from the curse of dimensionality. It requires an excessive amount of informative observations as well as an extreme memory for storing the corresponding sufficient statistic. The paper bypasses this problem by considering a rich subset of Markov-chain models, namely, mixtures of low dimensional Markov chains, possibly with external variables. It uses Bayesian approximate estimation suitable for a subsequent decision making under uncertainty. The proposed recursive (sequential, one-pass) estimator updates a product of Dirichlet probability densities (pds) used as an approximate posterior pd, projects the result back to this class of pds and applies an improved data-dependent stabilised forgetting, which counteracts the dangerous accumulation of approximation errors.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BC - Theory and management systems
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/GA13-13502S" target="_blank" >GA13-13502S: Fully Probabilistic Design of Dynamic Decision Strategies for Imperfect Participants in Market Scenarios</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information Sciences
ISSN
0020-0255
e-ISSN
—
Volume of the periodical
326
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
188-201
UT code for WoS article
000363348400013
EID of the result in the Scopus database
2-s2.0-84943770986