Bayesian statistics treats unknown parameters in a model as random and is popular in engineering for incorporating expert opinions. The posterior distribution summarizes experiments for Bayesians, who use credible sets for interval estimators. These estimators are assessed using the posterior distribution. Bayesian tests compare probabilities of different parameter regions. Choosing the right prior is crucial in Bayesian analysis, while computation is a major challenge in implementation.