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390043 UK VGSCO Statistical Inference via Convex Optimization (2018S)

Prüfungsimmanente Lehrveranstaltung

An/Abmeldung

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine

Block, May 23 – June 4, 2018
Seminar Room 3.307 (3rd floor, Faculty of Business, Economics and Statistics)

Wednesday, 23.05. 10:00 - 12:30
Thursday, 24.05. 10:00 - 12:30
Friday, 25.05. 10:00 - 12:30

Tuesday, 29.05. 15:00 - 17:30
Wednesday, 30.05. 10:00 - 12:30
Friday, 01.06. 10:00 - 12:30
Monday, 04.06. 10:00 - 12:30


Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Many inferences in Statistics, e.g., Maximum Likelihood Estimates, reduce to optimization. However, in MLE optimization is used for number crunching only and has nothing to do with motivation and performance analysis of the estimate. Most of traditional applications of Optimization in Statistics are of similar ``number crunching'' nature; they are beyond the scope of the course.
What is in the scope of the course, are inference routines motivated and justified by Optimization Theory (Convex Analysis, Conic Programming, Saddle Points, Duality...), the working horse being convex optimization.
This choice is motivated by
- nice geometry of convex sets, functions, and optimization problems
- computational tractability of convex optimization implying computational efficiency of statistical inferences stemming from Convex Optimization.

For more comments on "course's philosophy'' and for detailed description of course's contents, see Preface and Table of Contents in Lecture Notes available at
https://www2.isye.gatech.edu/~nemirovs/StatOpt_LN.pdf

Art der Leistungskontrolle und erlaubte Hilfsmittel

To be graded, a participant should submit at the end of the classes solutions to two Exercises from Lecture Notes available at https://www2.isye.gatech.edu/~nemirovs/StatOpt_LN.pdf (selection of Exercises to be solved is up to participant). Preparing solutions is a take-home task with no restrictions on material used.
The grade will be based on the quality of the solution as assessed by the lecturer.

Mindestanforderungen und Beurteilungsmaßstab

Prüfungsstoff

Course contents as reflected in Exercises from Lecture Notes.

Literatur

a) Lecture Notes: Anatoli Juditsky, Arkadi Nemirovski "Statistical Inferences via Convex Optimization''
https://www2.isye.gatech.edu/~nemirovs/StatOpt_LN.pdf

b) Transparencies
https://www2.isye.gatech.edu/~nemirovs/SCOTransp.pdf

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 31.08.2018 08:43