BYU

Abstract by Jackson Curtis

Personal Infomation


Presenter's Name

Jackson Curtis

Co-Presenters

None

Degree Level

Masters

Co-Authors

Richard Warr

Abstract Infomation


Department

Statistics

Faculty Advisor

Richard Warr

Title

Modeling System Reliability using Bayesian Non-Parametric Methods

Abstract

Data for system reliability is often in demand but is limited and expensive.  There is a need for statistical methods to use component, subsystem, and other data to supplement the limited system data.  A Bayesian framework is ideally suited for such a task.  Additionally, utilizing the conjugate properties of some Bayesian nonparametric models provides computational advantages over parametric methods.  In this work we explore the approximations made when combining components using these nonparametric methods.  Fitting occurs using the first and second moment of the probability of failure.  We find when the approximations are best, and when they breakdown.