Bayes' Theorem Calculator
Calculate conditional probability P(A|B) using priors and likelihoods.
Unconditional Probabilities (Priors)
Probability that the hypothesis A is true before evidence
P(not A) = 0%
Conditional Probabilities (Likelihoods)
Probability of evidence B, GIVEN A is true (Sensitivity)
Probability of evidence B, GIVEN A is false
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Posterior Probability P(A|B)
0%
Chance that A is true given B occurred
Probability Tree
A (0%)
B: 0%
P(A∩B)=0.00%
~A (0%)
B: 0%
P(~A∩B)=0.00%
P(A|B) = P(A∩B) / [P(A∩B) + P(~A∩B)]
Contextual Interpretation
If 0% of the population has a condition, and a test is 0% accurate (sensitivity), with a 0% false positive rate...
A person testing positive only has a 0% chance of actually having the condition.
Documentation
Bayes' Theorem
Bayes' theorem describes probability of an event, based on prior knowledge of conditions that might be related to the event.
Bayes' Theorem
Total Probability
Posterior
Frequently Asked Questions
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