Course syllabus adopted 2023-02-14 by Head of Programme (or corresponding).
Overview
- Swedish namePrinciper för statistisk slutledning
- CodeMVE326
- Credits7.5 Credits
- OwnerMPENM
- Education cycleSecond-cycle
- Main field of studyMathematics
- DepartmentMATHEMATICAL SCIENCES
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 20157
- Open for exchange studentsNo
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0115 Written and oral assignments, part A 2.5 c Grading: UG | 0 c | 0 c | 2.5 c | 0 c | 0 c | 0 c | |
| 0215 Examination, part B 5 c Grading: TH | 0 c | 0 c | 5 c | 0 c | 0 c | 0 c |
|
In programmes
Examiner
Umberto Picchini- Senior Lecturer, Applied Mathematics and Statistics, Mathematical Sciences
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.The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.