Bayesian Nonparametric Modeling of Higher Order Markov Chains

Published

Journal Article

© 2016 American Statistical Association. We consider the problem of flexible modeling of higher order Markov chains when an upper bound on the order of the chain is known but the true order and nature of the serial dependence are unknown. We propose Bayesian nonparametric methodology based on conditional tensor factorizations, which can characterize any transition probability with a specified maximal order. The methodology selects the important lags and captures higher order interactions among the lags, while also facilitating calculation of Bayes factors for a variety of hypotheses of interest. We design efficient Markov chain Monte Carlo algorithms for posterior computation, allowing for uncertainty in the set of important lags to be included and in the nature and order of the serial dependence. The methods are illustrated using simulation experiments and real world applications. Supplementary materials for this article are available online.

Full Text

Duke Authors

Cited Authors

  • Sarkar, A; Dunson, DB

Published Date

  • October 1, 2016

Published In

Volume / Issue

  • 111 / 516

Start / End Page

  • 1791 - 1803

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.2015.1115763

Citation Source

  • Scopus