Applications of the Weibull-Bayesian Distribution in Life Data Analysis

One of the new features of Weibull++ 7 is support for Bayesian statistics. In Version 6, all types of analysis dealt exclusively with classical statistics. The premise of Bayesian statistics is to incorporate prior knowledge along with a given set of current observations in order to make statistical inferences. The prior information could come from operational or observational data, from previous comparable experiments or from engineering knowledge.

This type of analysis is particularly useful if there is a lack of current test data and when there is a strong prior understanding about the parameter of the assumed life model and a distribution can be used to model the parameter. By incorporating prior information about a parameter, a posterior distribution for a parameter can be produced and an adequate estimate of reliability can be obtained. Weibull++ 7 introduces a new type of distribution that combines the properties of the Weibull distribution with the concepts of Bayesian statistics. The new distribution is called Weibull-Bayesian. The Reliability Basics section of this HotWire issues introduces the theory behind the Weibull-Bayesian model. This article presents two examples that illustrate the application of the Weibull-Bayesian distribution.

Introduction to the Weibull-Bayesian Distribution

This article describes Bayesian concepts, their applications in life data analysis and the Weibull-Bayesian distribution introduced in Weibull++ 7.