Bayesian nonparametric estimation of first passage distributions in semi-Markov processes

被引：1

作者：

Warr, Richard L. ^{[1
]}

Woodfield, Travis B. ^{[1
]}

机构：

[1] Brigham Young Univ, Dept Stat, Provo, UT 84602 USA

来源：

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | 2020年 / 36卷 / 02期

关键词：

Dirichlet process; multistate models; reliability; statistical flowgraph model; survival analysis; RENEWAL PROCESSES; MODELS; RELIABILITY; INFERENCE; ASTHMA;

D O I：

10.1002/asmb.2486

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

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.

引用

页码：237 / 250

页数：14

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