STAT 466
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Bayesian Reliability
Statistics
College of Computational, Mathematical & Physical Sciences
Course Description
Mathematics, distributions, management, and maintenance of basic reliability concepts; collection and analysis of test data; fault tree analysis; applying reliability in various areas.
When Taught
Winter.
Min
3
Fixed
3
Fixed
3
Fixed
2
Title
Reliability Concepts
Learning Outcome
a - Become familiar with basic reliability concepts and terminology b - Discuss the challenges of modeling censored data
Title
Reliability Data
Learning Outcome
a - Know the attributes of and the situations in which the four main types of reliability data (pass/fail data, failure count data, lifetime data and degradation data) arise b - Be able to identify different types of reliability data
Title
Statistical Modeling of Reliability Data
Learning Outcome
a - Know the basic models for each type of reliability data b - Know how to construct likelihoods for reliability data c - Be able to construct likelihoods for censored lifetime data and explain the rationale
Title
Bayesian Modeling
Learning Outcome
a - Know the different types of prior distributions b - Recognize and identify the influence of prior distributions in an analysis c - Use Bayesian modeling for inference and prediction from reliability data d - Know how to sample from a variety of posterior distributions e - Model censored lifetime data with standard parametric distributions
Title
Advanced concepts
Learning Outcome
Advanced concepts may include: a - Be able to incorporate covariates into reliability models and interpret the results b - Analyze data from accelerated life tests c - Understand and apply the basic concepts of system reliability d - Understand the basic terms and concepts of reliability from a repairable systems context e - Understand the concepts of producer's risk and consumer's risk in assurance testing
