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.
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This material is based upon work supported by the National Science Foundation under Grant No. 1811933.
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Bennett, I., Martin, D.E.K. & Lahiri, S.N. Fitting sparse Markov models through a collapsed Gibbs sampler. Comput Stat 38, 1977–1994 (2023). https://doi.org/10.1007/s00180-022-01310-8
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DOI: https://doi.org/10.1007/s00180-022-01310-8