STATS 608 - Optimization Methods in Statistics
Section: 001
Term: WN 2018
Subject: Statistics (STATS)
Department: LSA Statistics
Waitlist Capacity:
Advisory Prerequisites:
MATH 451, STATS 425, STATS 426. Comp programming experience recommended.
May be elected twice for credit.
Primary Instructor:

This course is an advanced introduction to deterministic (Part I) and stochastic (Part II) optimization techniques. Part I course topics include: basic result's from mathematical analysis, role of convexity in optimization, Karush-Kuhn-Tucker conditions in constrained optimization, majoration algorithms and their applications (EM algorithm), Newton's method and extensions, convergence results, convex programming and duality. The material covers both theoretical and implementation issues, as well as application to statistical models. Part II course topics include: basic Monte Carlo methods (random number generators, variance reductions techniques), an introduction to Markov chains (irreducibility, recurrence, ergodicity), Markov Chain Monte Carlo methods (Metropolis-Hastings and Gibbs sampling algorithms, data-augmentation techniques, convergence diagnostics), and stochastic optimization (simulated annealing and stochastic approximation). This part of the course covers both theory and applications to complex statistical models.

STATS 608 - Optimization Methods in Statistics
Schedule Listing
001 (LEC)
21STATS PhD only
MW 4:00PM - 5:30PM
NOTE: Data maintained by department in Wolverine Access. If no textbooks are listed below, check with the department.

ISBN: 9780387402642
Inference in hidden Markov models, Author: Olivier Cappe; Eric Moulines; Tobias Ryden., Publisher: Springer 2., korr. 2005
ISBN: 9780387212395
Monte Carlo statistical methods, Author: Robert, Christian P., 1961-, Publisher: Springer 2004
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