Selected solutions for the book are compiled here.

This chapter introduces the fundamental definitions of reliability and gives examples of common types of reliability data.

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- Chapter 1 Data Sets

Chapter 2: Bayesian Inference

In this chapter we review the fundamental concepts of Bayesian and likelihood-based inference in reliability. We explore prior distributions, sampling distributions, posterior distributions, and the relation between the three quantities as specified through Bayes' Theorem. We also provide examples of inference in both discrete and continuous settings.

- Solutions to Chapter 2 Exercises
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We extend the model structures described in the previous chapters using Bayesian hierarchical models. Because we generally cannot write the posterior distributions that result from these more complicated models in closed form, we begin this chapter with a description of Markov chain Monte Carlo algorithms that can be used to generate samples from intractable posterior distributions. These samples provide the basis for subsequent model inference. We also discuss empirical Bayes' methods. Finally, we describe techniques for assessing the sensitivity of model inferences to prior assumptions and a broadly applicable model diagnostic.

- Solutions to Chapter 3 exercises
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- Solutions to selected Chapter 4 exercises
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- Solutions to Chapter 5 exercises
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- Table 7.1
- Table 7.3
- Table 7.6
- Table 7.8
- Table 7.10
- Table 7.16 (including table 7.17)
- Table 7.19
- Table 7.22 (including table 7.23)
- Table 7.25
- Table 7.28
- Table 7.29
- Table 7.30
- Table 7.31
- Table 7.32
- Table 7.33
- Table 7.34
- Table 7.36
- Table 7.37
- Table 7.38
- Table 7.39
- Table 7.40
- Table 7.41
- Table 7.42

While reliability analysts have long used lifetime data for product/process reliability assessments, they began to employ degradation data for the same purpose in the 1990s. Assessing reliability with degradation data has a number of advantages. The analyst does not have to wait for failures to occur and can use less acceleration to collect degradation data. This chapter explains how to assess reliability using degradation data and also discusses how to accommodate covariates such as acceleration factors that speed up degradation and experimental factors that impact reliability in reliability improvement experiments. We also consider situations in which degradation measurements are destructive and conclude by introducing alternative stochastic models for degradation data.

- Solutions to selected Chapter 8 exercises
- Chapter 8 Data Sets

- Solutions to selected Chapter 9 exercises
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- Chapter 10 Data Sets