MATH-562 / 5 credits

Teacher: Davison Anthony Christopher

Language: English

## Summary

Inference from the particular to the general based on probability models is central to the statistical method. This course gives a graduate-level account of the main ideas of statistical inference.

## Keywords

Bayesian inference; calibration; data; decision theory; evidence; likelihood inference.

## Required courses

Courses on basic probability and statistics (e.g., MATH-240, MATH-230) and a first course on the linear model (e.g., MATH-341).

## Important concepts to start the course

Basic statistical background.

## Learning Outcomes

By the end of the course, the student must be able to:

• Formulate a statistical model suitable for a given situation
• Analyze the properties of a statistical inference procedure
• Assess / Evaluate the adequacy of a statistical formulation
• Assess / Evaluate the evidence for a statistical hypothesis

## Transversal skills

• Assess one's own level of skill acquisition, and plan their on-going learning goals.
• Continue to work through difficulties or initial failure to find optimal solutions.
• Demonstrate a capacity for creativity.
• Demonstrate the capacity for critical thinking

Slides and board

## Expected student activities

Attending lectures and problem classes; interacting in class; tackling problem sheets.

## Assessment methods

Final exam. Maybe a mid-term test.

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

## Supervision

 Office hours No Assistants Yes Forum Yes

No

## Bibliography

Cox, D. R. (2006)  Principles of Statistical Inference

Cox, D. R. and Hinkley, D. V. (1974) Theoretical Statistics

Davison, A. C. Statistical Models

## Notes/Handbook

Will be provided on Moodle.

## In the programs

• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical inference
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical inference
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical inference
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical inference
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical inference
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks

## Reference week

 Mo Tu We Th Fr 8-9 9-10 10-11 11-12 12-13 13-14 CE1100 14-15 15-16 CE1100 16-17 17-18 18-19 19-20 20-21 21-22

Thursday, 13h - 15h: Lecture CE1100

Thursday, 15h - 17h: Exercise, TP CE1100