MML-10-04: Maximum A Posteriori (MAP) Estimation & Bayesian Point Estimators – Practice Quiz

35 worked single-select and multiselect questions on Maximum A Posteriori (MAP) Estimation & Bayesian Point Estimators. Intended level: Tier 2–4. Prerequisites: QS 10.3, Part 09.

The sequence develops conceptual interpretation, explicit calculations, ML diagnostics and, where assigned, derivations with carefully stated assumptions. Feedback explains each answer and misconception by content.

Learning objectives: Optimize posterior density with the prior and likelihood both included. Compute MAP for discrete and continuous parameter examples. Derive Gaussian-prior quadratic penalties with correct scaling. Derive Laplace-prior absolute penalties and subgradient solutions. Choose posterior point summaries under explicit losses. Distinguish MAP plug-in prediction from posterior averaging. Analyze transformation dependence and large-sample caveats.

Study references: Deisenroth, Faisal & Ong, Mathematics for Machine Learning; Goodfellow, Bengio & Courville, Deep Learning.