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Syllabus for

Academic year
MVE325 - Statistical inference principles
 
Syllabus adopted 2014-02-13 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

This course round is cancelled


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0109 Examination 7,5 c Grading: TH   7,5 c    

Examiner:

Professor  Rebecka Jörnsten



  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

MVE 155 Statistical inference or MVE060 Statistics and data analysis
plus at least one of the courses
MVE185 Computer intensive statistical methods
MVE205 Survival analysis
MVE210 Linear mixed models for longitudinal data
TMS041 Multivariate statistical analysis
TMS031 Experimental design
TMS121 Statistics in genetics

Aim

This course takes an advanced and rigorous look at mathematical
statistics and approaches to inference. In addition to covering central
concepts and models of statistics, differing philosophical perspectives on scientific inference are discussed and compared.

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

After completing the course, the student will be able to explain and apply
the concepts and theorems mentioned in the course content below.

Content

Main topics of the course:
• exponential families of probability distributions,
• the sufficiency and likelihood principles of data reduction,
• maximum likelihood estimators and Bayes estimators,
• EM algorithm
• likelihood ratio tests and Bayesian tests,
• most powerful tests,
• interval estimators,
• asymptotic evaluation.

Organisation

Lectures, reading assignments, exercise assignments.

Literature

The course literature is given on a separate list.

Examination

Written examination.


Page manager Published: Mon 28 Nov 2016.