Law of Total Probability & Bayes' Theorem

Practice total probability and Bayes updating using source attribution, alerts and explicitly hypothetical screening scenarios. Interpret sensitivity, specificity, predictive values and likelihood ratios, then investigate base rates, repeated evidence, missing parameters and population shifts. Prerequisites: conditional probability, independence and elementary algebra. Diagnostic examples are stipulated mathematical models. Includes single-select and multiselect questions with worked solutions and distractor feedback.

References: 1.4.2 Law of total probability, 1.4.3 Bayes rule.