Bayesian Conditional Probability Explorer

See how priors and likelihoods determine posteriors

Prior: c(H) 0.30
c(E | H) 0.80
c(E | ¬H) 0.20
c(E) = 0.38  (determined by the settings above)
0.63
|
0.12
Bayes' Theorem (ratio form)
Log-odds form:
How to read this diagram: The total square represents the full probability space. The width of the left column is c(H) — the prior. Within each column, the top region is the probability of observing E given that column's truth value. Hover over terms in the formula to highlight the corresponding regions in the diagram. Use the Next → button to step through the Bayes' theorem calculation.