Bayesian statistics is a branch of statistics based on Bayes' theorem, which describes the probability of an event based on prior knowledge of conditions related to the event. Unlike frequentist approaches that interpret probability as the long-run frequency of events, Bayesian methods treat probability as a measure of belief or certainty that can be updated with new data.
Central to Bayesian statistics is the use of prior distributions, which represent initial beliefs about a parameter before data is observed, and posterior distributions, which update those beliefs after data is incorporated. This iterative process makes Bayesian inference especially useful in fields that require continuous learning, such as machine learning, epidemiology, and decision theory.
Bayesian methods have also influenced computational approaches like Markov Chain Monte Carlo (MCMC), enabling estimation of complex models that are difficult to solve analytically. The paradigm’s flexibility and emphasis on uncertainty make it a powerful tool for probabilistic reasoning.
Recommended to my by 📝Savannah Dale when talking about 📝Recursive Mythic Engines.