# Bayesian updating definition

In this module, we review the basics of probability and Bayes’ theorem.In Lesson 1, we introduce the different paradigms or definitions of probability and discuss why probability provides a coherent framework for dealing with uncertainty.In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred.Bayes' theorem thus gives the probability of an event based on new information that is, or may be related, to that event.The formula can also be used to see how the probability of an event occurring is affected by hypothetical new information, supposing the new information will turn out to be true.For instance, say a single card is drawn from a complete deck of 52 cards.This module introduces concepts of statistical inference from both frequentist and Bayesian perspectives.

Applications of the theorem are widespread and not limited to the financial realm.Posterior probability is the revised probability of an event occurring after taking into consideration new information.Posterior probability is calculated by updating the prior probability by using Bayes' theorem.This framework is extended with the continuous version of Bayes theorem to estimate continuous model parameters, and calculate posterior probabilities and credible intervals.In this module, you will learn methods for selecting prior distributions and building models for discrete data.

Applications of the theorem are widespread and not limited to the financial realm.Posterior probability is the revised probability of an event occurring after taking into consideration new information.Posterior probability is calculated by updating the prior probability by using Bayes' theorem.This framework is extended with the continuous version of Bayes theorem to estimate continuous model parameters, and calculate posterior probabilities and credible intervals.In this module, you will learn methods for selecting prior distributions and building models for discrete data.Lesson 5 introduces the fundamentals of Bayesian inference.