MATH-425 / 5 credits

Teacher:

Language: English

Remark: pas donné en 2020-21


Summary

In this course we will focus on stochastic approaches for modelling phenomena taking place in multivariate spaces. Our main focus will be on random field models and on statistical methods for model-based spatial statistics.

Content

Keywords

Random fields 

Kriging

Positive definite kernels

Conditional simulation

Experimental Design

Learning Prerequisites

Important concepts to start the course

Linear Algebra

Basics in probability and statistics 

Hilbert spaces

Notions of programming (Illustrations and computer labs in R along the semester; possible use of other languages to be discussed with the lecturer)

Assessment methods

Combined continuous and final assessment 

The nature of the final exam (oral or written) will be decided based on the number of students. 

"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."

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Spatial statistics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Spatial statistics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Spatial statistics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Spatial statistics
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22