Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/243340
Type: Artigo de evento
Title: Combining Multivariate Markov Chains
Author: Garcia
Jesus E.
Abstract: In this paper we address the problem of modelling multivariate finite order Markov chains, when the dataset is not large enough to apply the usual methodology. The number of parameters needed for a multivariate Markov chain grows exponentially with the process order and dimension of the chain's alphabet. Usually, when the data set is small, the order of the fitted model is reduced compared to the true process order. In this paper we introduce a strategy to estimate a multivariate process, through this new strategy the estimated order will be greater than the order achieved using standard statistical procedures. We apply the partition Markov models, which is a family of models, where each member is identified by a partition of the state space. The procedure consist in obtaining a partition of the state space that is constructed from a combination of the partitions corresponding to the marginal processes of the multivariate chain, and the partition corresponding to the multivariate Markov chain.
Subject: Selection
Country: MELVILLE
Editor: AMER INST PHYSICS
Citation: Combining Multivariate Markov Chains. Amer Inst Physics, v. 1648, p. 2015.
Rights: embargo
Identifier DOI: 10.1063/1.4912373
Address: https://www.google.com.br/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwj39a6W1MPMAhUGj5AKHRDaDqMQFgggMAA&url=http%3A%2F%2Fneuromat.numec.prp.usp.br%2Frelatorio%2Fartigos%2Fgarcia_2014_2.pdf&usg=AFQjCNG57lQiVg0Miai7jSaUwOPZbVbj7w
Date Issue: 2015
Appears in Collections:Unicamp - Artigos e Outros Documentos

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