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Bayesian Regularization of the Length of Memory in Reversible Sequences

Accepted version
Peer-reviewed

Repository DOI


Type

Article

Change log

Authors

Bacallado, Sergio 
Pande, Vijay 
Favaro, Stefano 
Trippa, Lorenzo 

Abstract

jats:titleSummary</jats:title> jats:pVariable order Markov chains have been used to model discrete sequential data in a variety of fields. A host of methods exist to estimate the history-dependent lengths of memory which characterize these models and to predict new sequences. In several applications, the data-generating mechanism is known to be reversible, but combining this information with the procedures mentioned is far from trivial. We introduce a Bayesian analysis for reversible dynamics, which takes into account uncertainty in the lengths of memory. The model proposed is applied to the analysis of molecular dynamics simulations and compared with several popular algorithms.</jats:p>

Description

Keywords

Bayesian analysis, reinforced random walk, reversibility, variable order Markov model

Journal Title

Journal of the Royal Statistical Society Series B: Statistical Methodology

Conference Name

Journal ISSN

1369-7412
1467-9868

Volume Title

Publisher

Oxford University Press (OUP)
Sponsorship
SF is supported by the European Research Council through grant StG N-BNP 306406, LT has been supported by the Claudia Adams Barr Program in Innovative Cancer Research and SB received funding from the Stein Fellowship.