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Syllabus for

Academic year
MVE326 - Statistical inference principles  
 
Syllabus adopted 2015-02-16 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 given every other year. Is given 2015/2016 but not 2016/2017


Teaching language: English

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0115 Written and oral assignments, part A 2,5c Grading: UG   2,5c    
0215 Examination, part B 5,0c Grading: TH   5,0c   09 Jun 2016 am M   14 Mar 2016 am H  

In programs

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

Examiner:

Docent  Annika Lang


Replaces

MVE325   Statistical inference principles


  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
the concepts and theorems mentioned in the course content. The student will be able to apply these concepts and theorems in theoretical exercises and programming tasks.

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 assignments. Written examination.


Page manager Published: Mon 28 Nov 2016.