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Syllabus for

Academic year
MVE326 - Statistical inference principles  
Principer för statistisk slutledning
 
Syllabus adopted 2020-03-12 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES

The course round is cancelled. For further questions, please contact the director of studies MPENM: ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, contact information can be found here. This course round is planned to be given every other year.


Teaching language: English
Application code: 20116
Open for exchange students: No

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0115 Written and oral assignments, part A 2,5 c Grading: UG   2,5 c    
0215 Examination, part B 5,0 c Grading: TH   5,0 c    

In programs

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

Examiner:

Umberto Picchini

  Go to Course Homepage


Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Knowledge corresponding to the course MVE155 Statistical Inference is required. In addition, knowledge corresponding to at least 15 credits in mathematical statistics at the second cycle level is required.

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 have understood the mathematical foundations of
  • point estimation including finding and evaluating estimators,
  • hypothesis testing including finding and evaluating test,
  • interval estimation including finding and evaluating estimators,
  • asymptotic evaluation,

and will be able to apply them 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 including compulsory elements

Written assignments. Written examination.


Page manager Published: Mon 28 Nov 2016.