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., The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes, <<STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES>>, 2020; (24): 1-15. [doi:10.1007/s11203-020-09224-2] [https://hdl.handle.net/10807/313364]
The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes
Peluso, Stefano;
2020
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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
