Bayesian regularization of the length of memory in reversible sequences

• 成果类型: Article
• 署名作者: Bacallado, Sergio; Pande, Vijay; Favaro, Stefano; Trippa, Lorenzo
• 署名单位: Stanford University; University of Turin; Collegio Carlo Alberto; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12140
• 发表日期: 2016
• 页码: 933-946
• 关键词: variable-order

摘要

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.