TY - JOUR
T1 - The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes
AU - Arfe, A.
AU - Peluso, Stefano
AU - Muliere, P.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Bayesian nonparametric
KW - Beta-Stacy
KW - Reinforced processes
KW - Semi-Markov
KW - Urn model
KW - Bayesian nonparametric
KW - Beta-Stacy
KW - Reinforced processes
KW - Semi-Markov
KW - Urn model
UR - https://publicatt.unicatt.it/handle/10807/313364
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85088781559&origin=inward
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85088781559&origin=inward
U2 - 10.1007/s11203-020-09224-2
DO - 10.1007/s11203-020-09224-2
M3 - Article
SN - 1387-0874
SP - 1
EP - 15
JO - Statistical Inference for Stochastic Processes
JF - Statistical Inference for Stochastic Processes
IS - 24
ER -