The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes
Statistics Theory
2020-07-24 v2 Methodology
Statistics Theory
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.
Cite
@article{arxiv.1812.00260,
title = {The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes},
author = {Andrea Arfè and Stefano Peluso and Pietro Muliere},
journal= {arXiv preprint arXiv:1812.00260},
year = {2020}
}
Comments
Accepted for publication in the journal Statistical Inference for Stochastic Processes on July 23, 2020