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Bayesian regularization of the length of memory in reversible sequences

dc.creatorBacallado, Sergio
dc.creatorPande, Vijay
dc.creatorFavaro, Stefano
dc.creatorTrippa, Lorenzo
dc.description.abstractVariable 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.
dc.publisherJournal of the Royal Statistical Society: Series B (Statistical Methodology)
dc.subjectBayesian analysis
dc.subjectreinforced random walk
dc.subjectvariable order Markov model
dc.titleBayesian regularization of the length of memory in reversible sequences

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