Update the probability of a hypothesis as more evidence or information becomes available.
Prior & Likelihood
Bayesian Update
Posterior Probability P(A|B)16.10%Updated probability given evidence B
Prior
1.0%
Posterior
16.1%
Total Evidence P(B)5.90%
Hypothesis Not True P(¬A)99.00%
About this calculator
Overview
Bayes' Theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. It is a fundamental principle in Bayesian inference, allowing you to update your beliefs in light of new evidence.
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Pro Tips
The 'Prior' is your initial belief before seeing the new evidence (B).
Always consider how likely the evidence is even if the hypothesis is false (the false positive rate).
Check that all probabilities entered are between 0 and 1 inclusive.
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The Bayesian Formula
Bayes' Theorem calculates the posterior probability P(A|B) using the likelihood P(B|A), the prior P(A), and the total evidence P(B):
