MML-10-04: Maximum A Posteriori (MAP) Estimation & Bayesian Point Estimators
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](https://mml-book.github.io/book/mml-book.pdf); [Goodfellow, Bengio & Courville, Deep Learning](https://www.deeplearningbook.org/contents/ml.html).
Oct 31, 2026
