How it works
Apply Bayes’ theorem: posterior after a positive result = prior × true-positive rate / [prior × true-positive rate + (1−prior) × false-positive rate].
Input definitions
- Prior event probability (%)
- Enter the value in the labeled unit.
- Positive rate when event is present (%)
- Enter the value in the labeled unit.
- Positive rate when event is absent (%)
- Enter the value in the labeled unit.
A worked example
- Prior event probability (%)
- 10
- Positive rate when event is present (%)
- 90
- Positive rate when event is absent (%)
- 5
Assumptions & limits
Entered-data numeric worksheet. Floating-point calculations are rounded for display. Statistical results describe the entered sample and depend on the stated model assumptions.
Sources & review
Maintained by AnsRio. Methods and examples use automated checks; this does not imply independent professional certification.
Method updated 2026-10-17. Content checked against the stated method and examples. Our verification approach.
Common questions
Why can a highly accurate classifier still produce false positives?
The prior probability matters. When an event is rare, false positives from the much larger non-event group can make up a substantial share of positive results.
How does Bayesian binary-classifier posterior work?
Apply Bayes’ theorem: posterior after a positive result = prior × true-positive rate / [prior × true-positive rate + (1−prior) × false-positive rate].
What are the limits of Bayesian binary-classifier posterior?
Entered-data numeric worksheet. Floating-point calculations are rounded for display. Statistical results describe the entered sample and depend on the stated model assumptions.