Dana Kelly and Curtis Smith are both specialists in Bayesian inference for risk and reliability analysis, working at the Idaho National Laboratory, USA. They provide support to the Nuclear Regulatory Commission, NASA, the Joint Research Centre in Pettern, and others. They are the authors of numerous refereed publications in the field.

• 1. Introduction and Motivation.- 2. Introduction to Bayesian Inference.- 3. Bayesian Inference for Common Aleatory Models.- 4. Bayesian Model Checking.- 5. Time Trends for Binomial and Poisson Data.- 6. Checking Convergence to Posterior Distribution.- 7. Hierarchical Bayes Models for Variability.- 8. More Complex Models for Random Durations.- 9. Modeling Failure with Repair.- 10. Bayesian Treatment of Uncertain Data.- 11. Bayesian Regression Models.- 12. Bayesian Inference for Multilevel Fault Tree Models.- 13. Additional Topics.

Dana Kelly and Curtis Smith are both specialists in Bayesian inference for risk and reliability analysis, working at the Idaho National Laboratory, USA. They provide support to the Nuclear Regulatory Commission, NASA, the Joint Research Centre in Pettern, and others. They are the authors of numerous refereed publications in the field.

• 1. Introduction and Motivation.- 2. Introduction to Bayesian Inference.- 3. Bayesian Inference for Common Aleatory Models.- 4. Bayesian Model Checking.- 5. Time Trends for Binomial and Poisson Data.- 6. Checking Convergence to Posterior Distribution.- 7. Hierarchical Bayes Models for Variability.- 8. More Complex Models for Random Durations.- 9. Modeling Failure with Repair.- 10. Bayesian Treatment of Uncertain Data.- 11. Bayesian Regression Models.- 12. Bayesian Inference for Multilevel Fault Tree Models.- 13. Additional Topics.

Dana Kelly and Curtis Smith are both specialists in Bayesian inference for risk and reliability analysis, working at the Idaho National Laboratory, USA. They provide support to the Nuclear Regulatory Commission, NASA, the Joint Research Centre in Pettern, and others. They are the authors of numerous refereed publications in the field.

• 1. Introduction and Motivation.- 2. Introduction to Bayesian Inference.- 3. Bayesian Inference for Common Aleatory Models.- 4. Bayesian Model Checking.- 5. Time Trends for Binomial and Poisson Data.- 6. Checking Convergence to Posterior Distribution.- 7. Hierarchical Bayes Models for Variability.- 8. More Complex Models for Random Durations.- 9. Modeling Failure with Repair.- 10. Bayesian Treatment of Uncertain Data.- 11. Bayesian Regression Models.- 12. Bayesian Inference for Multilevel Fault Tree Models.- 13. Additional Topics.