# Keynote:

Application of Bayesian Methods in Reliability Data Analyses

#### Dr. William Meeker

The development of the theory and application of Monte Carlo Markov Chain methods, vast
improvements in computational capabilities and emerging software alternatives have made it
possible for more frequent use of Bayesian methods in reliability applications. Bayesian
methods, however, remain controversial in Reliability (and some other applications) because of
the concern about where the needed prior distributions should come from. On the other hand,
there are many applications where engineers have solid prior information on certain aspects of
their reliability problems based on physics of failure or previous experience with the same failure
mechanism. For example, engineers often have useful but imprecise knowledge about the
effective activation energy in a temperature-accelerated life test or about the Weibull shape
parameter in the analysis of fatigue failure data. In such applications, the use of Bayesian
methods is compelling as it offers an appropriate compromise between assuming that such
quantities are known and assuming that nothing is known. In this talk, I will compare the use of
Bayesian methods with the traditional maximum likelihood methods for a group of examples
including the analysis of field data with multiple censoring, accelerated life test data, and
accelerated degradation test data.