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MATH 559 — Bayesian Theory and Methods

Subjective probability, Bayesian statistical inference and decision making, de Finetti’s representation. Bayesian parametric methods, optimal decisions, conjugate models, methods of prior specification and elicitation, approximation methods. Hierarchical models. Computational approaches to inference, Markov chain Monte Carlo methods, Metropolis—Hastings. Nonparametric Bayesian inference.

  • Credits: 4
  • Faculty: Faculty of Science
  • Department: Mathematics and Statistics
  • Taught by: David Stephens
  • Prerequisite: MATH 324, MATH 357, MATH 557, or equivalent, and MATH 208 or equivalent.

Sections offered

  • Section 001 (Lec), Tue Thu 4:05-5:25 pm — 3 seats open