Bayes posterior: screening example

Learning goal: Posterior depends on likelihood and prior prevalence. Even an accurate test can produce many false positives for a rare condition.

Prevalence Sensitivity Specificity A second conditionally independent positive

True positive False positive P(condition | results) =

Vary prevalence to inspect the base-rate effect. A repeated positive multiplies another likelihood ratio only under the specified conditional-independence assumption.