Fitting sparse Markov models through a collapsed Gibbs sampler

Iris Bennett (), Donald E. K. Martin and Soumendra Nath Lahiri
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Iris Bennett: North Carolina State University
Donald E. K. Martin: North Carolina State University
Soumendra Nath Lahiri: Washington University in St. Louis

Computational Statistics, 2023, vol. 38, issue 4, No 17, 1977-1994

Abstract: Abstract Sparse Markov models (SMMs) provide a parsimonious representation for higher-order Markov models. We present a computationally efficient method for fitting SMMs using a collapsed Gibbs sampler, the GSDPMM. We prove the consistency of the GSDPMM in fitting SMMs. In simulations, the GSDPMM was found to perform as well or better than existing methods for fitting SMMs. We apply the GSDPMM method to fit SMMs to patterns of wind speeds and DNA sequences.

Keywords: Markov chains; Model selection; Bayesian nonparametrics (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-022-01310-8

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