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Bayesian nonparametric estimation of first passage distributions in semi‐Markov processes
Author(s) -
Warr Richard L.,
Woodfield Travis B.
Publication year - 2019
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2486
Subject(s) - computation , computer science , markov chain , bayesian probability , estimation , nonparametric statistics , markov model , markov process , machine learning , algorithm , artificial intelligence , econometrics , mathematics , statistics , engineering , systems engineering
Bayesian nonparametric (BNP) models provide a flexible tool in modeling many processes. One area that has not yet utilized BNP estimation is semi‐Markov processes (SMPs). SMPs require a significant amount of computation; this, coupled with the computation requirements for BNP models, has hampered any applications of SMPs using BNP estimation. This paper presents a modeling and computational approach for BNP estimation in semi‐Markov models, which includes a simulation study and an application of asthma patients' first passage from one state of control to another.