Spatiotemporal Models for Sequential Data from Multiple Sources through Markov Cha...
Recurrent neural networks for classification of proteins into families and compari...
Grant number: | 16/13646-4 |
Support Opportunities: | Regular Research Grants |
Start date: | September 01, 2016 |
End date: | August 31, 2017 |
Field of knowledge: | Physical Sciences and Mathematics - Probability and Statistics - Probability |
Principal Investigator: | Diego Fernando de Bernardini |
Grantee: | Diego Fernando de Bernardini |
Host Institution: | Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
Abstract
In this research project we propose to investigate originally the problem of the comparison between the random fields of local times of a Markov chain which has a unique invariant probability measure and of a sequence of independent and identically distributed random variables with probability law given by the invariant measure of the chain, up to a specific moment, by studying the total variation distance between the laws of these random fields. To do so, we intend to obtain a coupling between the local times fields of the two mentioned processes, up to a given moment, using the technique of soft local times. As a consequence, we hope to obtain an upper bound for the refered total variation distance between the fields, which we expect to be reasonable and interesting under certain assumptions, and also small for a broad and important class of Markov chains involving such assumptions. (AU)
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