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Syllabus for

Academic year
MVE185 - Computer intensive statistical methods
 
Syllabus adopted 2013-02-21 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: English
Open for exchange students
Block schedule: X

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0108 Examination 7,5 c Grading: TH   7,5 c   26 Oct 2013 pm V,  Contact examiner

In programs

MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)

Examiner:

Doktor  Anders Sjögren


Course evaluation:

http://document.chalmers.se/doc/dfb07cde-ca47-442b-8d2e-7bcd12c6de7d


  Go to Course Homepage

Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

TMS150 Stochastic Data Processing and Simulation or a similar course.

Aim

The course is built around statistical models and computational tools in cases where
computer-assisted computations are essential. In particular, we focus on stochastic simulation
as a computational tool. Bayesian inference is a central concept, and we will study Markov chain
Monte Carlo simulation methods from both applied and theoretical viewpoints.
Jackknife and bootstrap methods will be studied, as well as
decision theory. Permutation methods for hypothesis testing, and multiple
hypothesis testing issues are also covered in this course.

A special effort will be made to help the student to see the connections and
interplay between statistical modeling and applied problem solving, as well
as computational and theoretical aspects of the models.

Learning outcomes (after completion of the course the student should be able to)

The goal of the course is to give the student familiarity with a range of modern computer intensive methods within statistical analysis. The student should be able to combine and see the connections between the modeling aspects, theoretical aspects, computational aspects, and applied aspects of each example problem. The student should be able to make independent and informed decisions about statistical modeling and computational choices, and to present his or her analysis in a structured and pedagogical way.

Content

The course is built around statistical models and computational tools where computer-assisted computations are essential. In particular, we focus on methods for simulation from stochastic models, and stochastic simulation as a computational tool. Bayesian inference is a central concept, and we study Markov chain Monte Carlo simulation methods from both applied and theoretical viewpoints. Jackknife and bootstrap methods are treated, as well as decision theory. Permutation methods for hypothesis testing, and multiple hypothesis testing issues are also covered. An effort will be made to help the student see the connections and interplay between statistical modeling and applied problem solving, and computational and theoretical aspects of the models.

Organisation

Lectures, classroom exercises and projects. Written examination.

Literature

Will be provided for each topic from lecture notes and publications

Examination

Each student must complete a number of computer based hand-in assignments. The grade will be based on a written examination at the end of the course.


Page manager Published: Mon 28 Nov 2016.