ECTS credits:
5 ECTS
Course parameters:
Language: English
Level of course: PhD course
Time of year: Spring 2024
No. of contact hours/hours in total incl. preparation, assignment or the like: 35/80
Capacity limits: 16 participants
Objectives of the course:
The PhD students will be introduced to Bayesian hierarchical modelling, which are becoming increasingly popular for fitting ecological, environmental, and human disease models to temporal and spatial data. The aim of the course is to introduce the students to i) the applied use of likelihood functions and Bayesian statistics, ii) setting up advanced hierarchical statistical models with latent variables, and iii) making quantitative predictions with a known degree of uncertainty.
Learning outcomes and competences:
At the end of the course, the student should be able to:
- assess the possible value of using advanced Bayesian methods in the students own scientific work
- critically evaluate scientific literature using advanced statistical models
Compulsory program:
preparation, active participation, assignment
Course contents:
Prerequisites:
Introductory probability and statistics courses
Name of lecturers:
Christian Damgaard and Peter Borgen Sørensen
Type of course/teaching methods:
Seminars and exercises using R
Literature:
Before course start the student are expected to have read chapters 1, 3-7 in the electronic book: https://bayesball.github.io/BOOK/probability-a-measurement-of-uncertainty.html,
and be familiar with the statistic software R (e.g. r.sund.ku.dk)
We will use “Bayesian Inference with INLA” (https://becarioprecario.bitbucket.io/inla-gitbook/) as course book including supplementary original literature.
Course homepage:
None
Course assessment:
Personalized reports (approximately 20-40 pages, corresponding to a workload of 20 hours outside, and in the week after the end of the scheduled classes) must be completed and submitted for approval (pass/fail).
Provider:
Department of Ecoscience, Aarhus University
Special comments on this course:
All expenses for accommodation and travel are paid by the individual PhD student.
Time:
21/3, 22/3, 25/3-27/3 2024
Place:
Department of Ecoscience, Aarhus University, Denmark
Registration:
Deadline for registration is 1/3 2024 (first come, first served).
For registration: Christian Damgaard, e-mail: cfd@ecos.au.dk
If you have any questions, please contact Christian Damgaard or Peter Borgen Sørensen
Course Program
The topics of the 5 days are as detailed below, and each topic starts with a lecture followed by computer exercises in R which are carried out in teams of two-three participants. Each participant must produce a personalized report of the exercises. During the course, the participants should be prepared to work outside the scheduled classes to complete the computer exercises.
Day 1
10:00 – 10:15 Coffee
10:15 – 12:00 Lecture 1: Welcome, Introduction to Course and introduction to R
12:00 – 13:00 Lunch
13:00 – 15:00 Lecture 2: Probability and likelihood functions, exercises in R
15:00 – 15:15 Coffee
15:15 – 16:00 Short plenum presentation of the students own data and methods.
Day 2
08:30 – 10:00 Lecture 3: Bayesian statistics, exercises in R
10:00 – 10:15 Coffee
10:15 – 12:00 Exercises in R
12:00 – 13:00 Lunch
13:00 – 15:00 Exercises in R
15:00 – 15:15 Coffee
15:15 – 16:00 Lecture 4: Probability theory – the logic of science
Day 3
08:30 – 10:00 Lecture 5: Bayesian hierarchical models
10:00 – 10:15 Coffee
10:15 – 12:00 Exercises in R
12:00 – 13:00 Lunch
13:00 – 15:00 Exercises in R
15:00 – 15:15 Coffee
15:15 – 16:00 Exercises in R
Day 4
08:30 – 10:00 Lecture 6: Examples of Bayesian hierarchical model
10:00 – 10:15 Coffee
10:15 – 12:00 Computer Exercises
12:00 – 13:00 Lunch
13:00 – 15:00 Computer Exercises
15:00 – 15:15 Coffee
15:15 – 16:00 Computer Exercises
Day 5
08:30 – 10:00 Lecture 7: Ecological predictions
10:00 – 10:15 Coffee
10:15 – 12:00 Evaluation in plenum to identify relevant methods for students own data.
12:00 – 13:00 Lunch
13:00 – 14:00 Evaluation and departure
Next Monday Submission of final report by e-mail to Christian Damgaard
