Spatial modelling and Bayesian inference

Last modified by jpvanhat@helsinki_fi on 2024/03/27 10:30

Spatial modelling and Bayesian inference, Spring 2017

Scope: 5 cr

Type: Advanced studies

Teaching: course exercises and Exam

Topics: Gaussian process, its properties and use in spatial statistics. Inference and prediction with hierarchical Gaussian process models. Maximum a posterior and Markov chain Monte Carlo approaches for inference.

Prerequisites: At least intermediate level of probability and statistical inference, including Bayesian inference, courses as well as linear algebra and matrix calculation. The course includes computer exercises so programming skills with R or Matlab are required.

The [toc] macro is a standalone macro and it cannot be used inline. Click on this message for details.
This macro generates standalone content. As a consequence you need to make sure to use a syntax that separates your macro from the content before and after it so that it's on a line by itself. For example in XWiki Syntax 2.0+ this means having 2 newline characters (a.k.a line breaks) separating your macro from the content before and after it.

Exam

Tuesday 9.5. at 12-15.

News

17.3.2017: Moodle for the course

We have now a Moodle platform for the course.

The course information will now be distributed mainly through Moodle and you can now also submit your weekly exercises through it. The weekly exercises and lecture notes will be updated also to the wiki-page (http://wiki.helsinki.fi/display/mathstatKurssit/Spatial+modelling+and+Bayesian+inference). However, news, answers to exercises, lecture slides and some demos will be distributed through Moodle only.

Moodle page url: https://moodle.helsinki.fi/course/view.php?id=24025
self-enroment key: bayes

Note for Student that do not have University of Helsinki account:
- you should be able to log in using "Haka login":
https://moodle.helsinki.fi/Shibboleth.sso/HAKALogin?target=https://moodle.helsinki.fi/auth/shibboleth/
- If Haka login does not work, send me an email and we fix the problem

Teaching schedule

The course takes place on period IV: 13.3.-7.5. The specific schedule is the following

Lectures:

Monday 10-12 room B221 in Exactum
Tuesday 14-16 room B321 in Exactum

Exercise classes:

Wednesday 14-16 room B221 in Exactum

Course assessment and grading

Grading: 1-5
- [The final grade] = 0.5*[the grade from exercises] + 0.5*[the grade from the exam]
Exercises and their grading
- <50% of weekly exercises correct = failed
- >50% of weekly exercises correct = 1
- >60% of weekly exercises correct = 2
- >70% of weekly exercises correct = 3
- >80% of weekly exercises correct = 4
- >90% of weekly exercises correct = 5
Exam and their grading
- Grading as with exercises
- You can use pencil and eraser in the exam.
Learning diary
- In order to pass the course you need to also write a learning diary (this will help in developing the course further)
- bookkeeping of time used for the course
- discuss what was easy/hard, which areas too much/little time was devoted to, what more should have been included, what should have been left out, etc.
- very informal, keep it short!

Course material

Various chapters from the book Gaussian processes in Machine Learning (Rasmussen and Williams, 2006), lecture notes and articles to be announced during the course. For additional reading the following book is suggested: Banerjee, S., Carlin, B. P. and Gelfand, A. (2015) Hierarchical Modeling and Analysis for Spatial Data, Second Edition, Chapman and Hall/CRC.

Useful links to R and Matlab and for their comparison:

http://www.math.umaine.edu/~hiebeler/comp/matlabR.pdf

http://mathesaurus.sourceforge.net/octave-r.html