Bayes theorem visualizer
Update a prior from stated likelihoods and see every branch of the expected-frequency tree.
Two mutually exclusive, exhaustive hypotheses. Enter probabilities from 0 to 100% and a reference total from 1 to one billion. Counts are expectations, not observations or rounded people. A posterior is undefined when its conditioning event has probability zero. This is a calculation from supplied assumptions, not a diagnosis or inferred likelihood. Change the input or settings to update your result. Results update as you type. Up to 1,000,000 characters.
Your result
How to use Bayes theorem visualizer
- Enter the prior probability and the likelihood of the evidence under each hypothesis.
- Choose a reference total to see expected frequencies.
- Read the evidence denominator before using the posterior, and download the full tree.
Useful next steps
Read the model
Update a prior from stated likelihoods and see every branch of the expected-frequency tree.
Check the definition
Two mutually exclusive, exhaustive hypotheses. Enter probabilities from 0 to 100% and a reference total from 1 to one billion. Counts are expectations, not observations or rounded people. A posterior is undefined when its conditioning event has probability zero. This is a calculation from supplied assumptions, not a diagnosis or inferred likelihood.
Keep the calculation
Download SVG for the chart, JSON for unrounded values, CSV for the table and the assumptions note. Your input stays on your device.