Best Practices in Relaibility Data Analysis Background
Best Practices in Reliability Data Analysis

Bayesian Inference in Reliability

According to Bill Meeker, PhD, Professor of Statistics at Iowa State University, we are in the midst of a revolution in the use of Bayesian methods for reliability analysis. Why? Because the data available to make inferences about reliability is sometimes very limited, leading to large uncertainty. Bayesian methods provide a formal way to combine available data with information from previous experience, resulting in greater precision.

During his presentation, Dr. Meeker uses an example of predicting rocket motor failure rates to help you gain a better understanding of:

  • The limitations of standard maximum likelihood methods.
  • The specific differences between likelihood-based and Bayes-based inferences.
  • The concept of "informative prior information," and how it can drastically improve precision.
See these techniques in action!

To learn more and see how Bayesian methods can be implemented in JMP, register below.

Bayesian Inference in Reliability in JMP

Hands-on Case Study

Join JMP product manager Leo Wright as he brings Dr. Meeker's examples to life using JMP software.

In this video, Leo Wright provides a step-by-step demonstration of how to perform Bayesian inference in JMP using the rocket motor example introduced by Dr. Meeker.

To explore these capabilities for yourself, download JMP for free for 30 days.

  Please subscribe me to JMP Newswire, the monthly newsletter for JMP users.
  Yes, you may send me emails occasionally about JMP products and services. I understand that I can withdraw my consent at any time by clicking the opt-out link in the emails.

JMP is a division of SAS Institute Inc. Your information will be handled in accordance with the SAS Privacy Statement.


Back to Top