Bayes' Theorem Probability

Introduction to Bayes' Theorem Calculator

Bayes' theorem is a way to find a probability when we know certain other probabilities. The formula is P(A|B), which is the probability of A happening given B has occurred, is the product of the prior probability of A(P(A)), the likelihood of B given A(P(B|A)), divided by the probability of B(P(B)). This theorem has applications in a wide range of fields from healthcare to machine learning.

Parameter usage:

• prior = Prior probability of A, denoted P(A)
• likelihood = Probability of B given A, denoted P(B|A)
• marginal = Marginal probability of B, denoted P(B)

Example valid values:

• prior = 0.3
• likelihood = 0.9
• marginal = 0.18

Output:

• posterior probability = P(A|B)

Data validation

All probabilities must be between 0 and 1.

Summary

This calculator determines the posterior probability of an event based on prior knowledge and the likelihood of related events.