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.

Arfe, A., Peluso, S., Muliere, P. (2021). The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes. STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES, 24(1), 1-15 [10.1007/s11203-020-09224-2].

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

Peluso S.;
2021

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.
Articolo in rivista - Articolo scientifico
Bayesian nonparametric; Beta-Stacy; Reinforced processes; Semi-Markov; Urn model;
English
29-lug-2020
2021
24
1
1
15
reserved
Arfe, A., Peluso, S., Muliere, P. (2021). The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes. STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES, 24(1), 1-15 [10.1007/s11203-020-09224-2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/282224
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