Bayesian inference is a method of statistical inference that uses Bayes’ Theorem to update the probability estimate for a hypothesis as new evidence or information becomes available. In this approach, you start with a prior probability (an initial belief about the parameter or hypothesis) and adjust it in light of observed data, producing a posterior probability. This updated probability reflects a combination of prior knowledge and new evidence, making Bayesian inference particularly useful for cases where uncertainty and prior beliefs play a significant role. It’s widely used in fields like machine learning, natural language processing, and decision-making under uncertainty. ^[From ChatGPT]