Introduction to Bayesian Inference

Bayesian inference is the process of arriving at estimates of the model parameters reflecting the blending of information from different sources. Most commonly, two sources of information are considered: prior knowledge or beliefs and observed data. The discrepancy (or lack thereof) between them and their relative strength determines how far away the resulting Bayesian estimate is from the corresponding classical estimate. Along with the point estimate, which most often is the posterior mean, in the Bayesian setting one has available the whole posterior distribution, allowing for a richer analysis.