Bayesian regularization of the length of memory in reversible sequences
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
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Bacallado, S., Pande, V., Favaro, S., & Trippa, L. (2015). Bayesian regularization of the length of memory in reversible sequences. Journal of the Royal Statistical Society: Series B (Statistical Methodology) https://doi.org/10.1111/rssb.12140
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.
Bayesian analysis, reinforced random walk, reversibility, variable order Markov model
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.
External DOI: https://doi.org/10.1111/rssb.12140
This record's URL: https://www.repository.cam.ac.uk/handle/1810/255089