Bayesian non-parametric simulation of hazard functions

Dmitriy Belyi; Elmira Popova; David Morton; Paul Damien

doi:10.1109/WSC.2009.5429359

Bayesian non-parametric simulation of hazard functions

Dmitriy Belyi^*, Elmira Popova, David Morton, Paul Damien

^*Corresponding author for this work

Industrial Engineering and Management Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

In Bayesian non-parametric statistics, the extended gamma process can be used to model the class of monotonic hazard functions. However, numerical evaluations of the posterior process are very difficult to compute for realistic sample sizes. To overcome this, we use Monte Carlo methods introduced by Laud, Smith, and Damien (1996) to simulate from the posterior process. We show how these methods can be used to approximate the increasing failure rate of an item, given observed failures and censored times. We then use the results to compute the optimal maintenance schedule under a specified maintenance policy.

Original language	English (US)
Title of host publication	Proceedings of the 2009 Winter Simulation Conference, WSC 2009
Pages	475-482
Number of pages	8
DOIs	https://doi.org/10.1109/WSC.2009.5429359
State	Published - Dec 1 2009
Event	2009 Winter Simulation Conference, WSC 2009 - Austin, TX, United States Duration: Dec 13 2009 → Dec 16 2009

Other

Other	2009 Winter Simulation Conference, WSC 2009
Country/Territory	United States
City	Austin, TX
Period	12/13/09 → 12/16/09

ASJC Scopus subject areas

Software
Modeling and Simulation
Computer Science Applications

Access to Document

10.1109/WSC.2009.5429359

Cite this

@inproceedings{36de1b19bd6d4f719f21ce5c4a9beb92,

title = "Bayesian non-parametric simulation of hazard functions",

abstract = "In Bayesian non-parametric statistics, the extended gamma process can be used to model the class of monotonic hazard functions. However, numerical evaluations of the posterior process are very difficult to compute for realistic sample sizes. To overcome this, we use Monte Carlo methods introduced by Laud, Smith, and Damien (1996) to simulate from the posterior process. We show how these methods can be used to approximate the increasing failure rate of an item, given observed failures and censored times. We then use the results to compute the optimal maintenance schedule under a specified maintenance policy.",

author = "Dmitriy Belyi and Elmira Popova and David Morton and Paul Damien",

year = "2009",

month = dec,

day = "1",

doi = "10.1109/WSC.2009.5429359",

language = "English (US)",

isbn = "9781424457700",

pages = "475--482",

booktitle = "Proceedings of the 2009 Winter Simulation Conference, WSC 2009",

note = "2009 Winter Simulation Conference, WSC 2009 ; Conference date: 13-12-2009 Through 16-12-2009",

}

TY - GEN

T1 - Bayesian non-parametric simulation of hazard functions

AU - Belyi, Dmitriy

AU - Popova, Elmira

AU - Morton, David

AU - Damien, Paul

PY - 2009/12/1

Y1 - 2009/12/1

N2 - In Bayesian non-parametric statistics, the extended gamma process can be used to model the class of monotonic hazard functions. However, numerical evaluations of the posterior process are very difficult to compute for realistic sample sizes. To overcome this, we use Monte Carlo methods introduced by Laud, Smith, and Damien (1996) to simulate from the posterior process. We show how these methods can be used to approximate the increasing failure rate of an item, given observed failures and censored times. We then use the results to compute the optimal maintenance schedule under a specified maintenance policy.

AB - In Bayesian non-parametric statistics, the extended gamma process can be used to model the class of monotonic hazard functions. However, numerical evaluations of the posterior process are very difficult to compute for realistic sample sizes. To overcome this, we use Monte Carlo methods introduced by Laud, Smith, and Damien (1996) to simulate from the posterior process. We show how these methods can be used to approximate the increasing failure rate of an item, given observed failures and censored times. We then use the results to compute the optimal maintenance schedule under a specified maintenance policy.

UR - http://www.scopus.com/inward/record.url?scp=77951575543&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951575543&partnerID=8YFLogxK

U2 - 10.1109/WSC.2009.5429359

DO - 10.1109/WSC.2009.5429359

M3 - Conference contribution

AN - SCOPUS:77951575543

SN - 9781424457700

SP - 475

EP - 482

BT - Proceedings of the 2009 Winter Simulation Conference, WSC 2009

T2 - 2009 Winter Simulation Conference, WSC 2009

Y2 - 13 December 2009 through 16 December 2009

ER -

Bayesian non-parametric simulation of hazard functions

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this