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Bayesian regularization of the length of memory in reversible sequences
Author(s) -
Bacallado Sergio,
Pande Vijay,
Favaro Stefano,
Trippa Lorenzo
Publication year - 2016
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12140
Subject(s) - computer science , markov chain , bayesian probability , algorithm , regularization (linguistics) , variety (cybernetics) , theoretical computer science , artificial intelligence , machine learning
Summary Variable 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.