The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes

Andrea Arfè (), Stefano Peluso and Pietro Muliere
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Andrea Arfè: Harvard Medical School
Stefano Peluso: University of Milano-Bicocca
Pietro Muliere: Bocconi University

Statistical Inference for Stochastic Processes, 2021, vol. 24, issue 1, No 1, 15 pages

Abstract: Abstract The literature on Bayesian methods for the analysis of discrete-time semi-Markov processes is sparse. In this paper, we introduce the semi-Markov beta-Stacy process, a stochastic process useful for the Bayesian non-parametric analysis of semi-Markov processes. The semi-Markov beta-Stacy process is conjugate with respect to data generated by a semi-Markov process, a property which makes it easy to obtain probabilistic forecasts. Its predictive distributions are characterized by a reinforced random walk on a system of urns.

Keywords: Bayesian nonparametric; Semi-Markov; Beta-Stacy; Reinforced processes; Urn model (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11203-020-09224-2

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