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Syllabus for

Academic year
MVE186 - Computer intensive statistical methods
 
Syllabus adopted 2015-02-11 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

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0114 Project 2,0 c Grading: UG   2,0 c    
0214 Examination 5,5 c Grading: TH   5,5 c   24 Oct 2015 pm V,  Contact examiner,  am J

In programs

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

Examiner:

Doktor  Jonas Wallin


Replaces

MVE185   Computer intensive statistical methods


  Go to Course Homepage

Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

Basic skills in mathematical statistics.
Basic skills in scientific programming (for example in Matlab or R) as achieved by completing TMS150 "Stochastic Data Processing and Simulation".

Aim

In modern Bayesian and classical statistical analysis and in decision theory, calculating exact results is often intractable due to the complexity of the involved models and their parameter spaces. This course aims at equipping the student with practical and theoretical skills for utilizing computationally intensive methods to solve such tasks, in particular in the form of stochastic simulations.

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

Basic theory and motivating problems from decision theory and Bayesian and classical statistical analysis are introduced. The course then contains a number of methods and techniques for solving such problems, for example: random variable generation, Monte Carlo integration, Markov chain Monte Carlo, Bootstrapping and the handling of missing data (both using the EM-algorithm and using Bayesian modeling). These methods are studied from both a theoretical and a practical perspective. Hands-on experience and understanding of the methods is practiced by applying them on different problems in a series of hand-in exercises.

Organisation

Lectures and computer based hand-in exercises. Written examination.

Literature

Introducing Monte Carlo Methods with R (2010) Robert & Casella, Springer (ISBN: 978-1-4419-1575-7). Is available in electronic format through the Chalmers library website. 
Excerpts from other publications.

Examination

Compulsory 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.