Posterior Probability
What is a Posterior Probability?
Posterior Probability refers to the probability derived by updating the prior probability in the presence of new evidence. It is based on the Bayes theorem.
What is Marginal Probability?
Marginal probability P(B) is used in Bayes’ theorem and can be calculated as the sum of the joint probabilities over all possible events:
P(B) = ΣP(B|A) × P(A)
Where the sum is taken over all possible events A.
What is Expected Posterior Probability?
Posterior probability represents the revised belief or confidence in a hypothesis or event after considering observed data.
Posterior Probability
Posterior probability is a key concept in Bayesian statistics that represents the updated probability of a hypothesis given new evidence. It combines prior beliefs with new data to provide a revised probability that incorporates the new information.
In this article, we have covered Posterior Probability definition, formula, example and others in detail.
Table of Content
- What Is a Posterior Probability?
- Bayes’ Theorem Formula
- Posterior Probability Formula
- Applications of Posterior Probability
- Example on Posterior Probability
- FAQs on Posterior Probability