Bayesian estimation and inference is a method that combines prior beliefs with new data to calculate uncertainty about parameters. The main focus is the posterior distribution, which shows uncertainty about parameters based on prior beliefs and data. Computational methods can provide accurate approximations of the posterior. Bayesian estimates include measures of central tendency and intervals where parameters likely fall. Bayesian hypothesis testing is complex, but the Bayes factor can help compare different hypotheses based on data probability.