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)
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.