Kaisa_2012_3_photo by Veikko Somerpuro

For information on how the teaching and course exams are arranged now in the time of corona see the course Moodle page.

Anmäl dig
10.2.2020 kl. 09:00 - 3.5.2020 kl. 23:59
Moodle
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Tidsschema

I den här delen hittar du kursens tidsschema. Kontrollera eventuella andra tider i beskrivning.

DatumTidPlats
mån 9.3.2020
10:15 - 12:00
tis 10.3.2020
09:15 - 11:00
fre 13.3.2020
12:15 - 14:00
mån 16.3.2020
10:15 - 12:00
tis 17.3.2020
09:15 - 11:00
fre 20.3.2020
12:15 - 14:00
mån 23.3.2020
10:15 - 12:00
tis 24.3.2020
09:15 - 11:00
fre 27.3.2020
12:15 - 14:00
mån 30.3.2020
10:15 - 12:00
tis 31.3.2020
09:15 - 11:00
fre 3.4.2020
12:15 - 14:00
mån 6.4.2020
10:15 - 12:00
tis 7.4.2020
09:15 - 11:00
fre 17.4.2020
12:15 - 14:00
mån 20.4.2020
10:15 - 12:00
tis 21.4.2020
09:15 - 11:00
fre 24.4.2020
12:15 - 14:00
mån 27.4.2020
10:15 - 12:00
tis 28.4.2020
09:15 - 11:00

Beskrivning

Optional course.

Master's Programme in Mathematics and Statistics is responsible for the course.

The course belongs to the Statistics and Social statistics module.

Students in Master's programmes of Mathematics and Statistics, Life Science Informatics and Data Science

Bachelor studies in mathematics and statistics or equivalent knowledge.

It is assumed that you have taken at least one of the following courses:

MAT22005 Bayesian inference

DATA11006 Statistical Data Science

LSI35002 Bayesian data analysis

or a course covering basics in Bayesian inference.

MAST32001 Computational statistics

MAST32005 Spatial modelling and Bayesian inference

The course covers some foundations of Bayesian statistics and its theoretical links to decision theory. After the course you will understand the justification and axiomatic construction for probability as a measure of subjective uncertainty and how this leads to Bayes theorem. You will also be introduced to properties of Bayesian inference in the limit of large data and to De'Finetti's Theorem and their basic consequences and interpretations. After the course you will understand what are model's marginal likelihood and Bayes factors, posterior predictive model comparison and validation, decision analysis and experimental design. You will also be able to apply these techniques to practical data analysis tasks.

After the Bachelor studies and Bayesian Inference course.
4th period

Week 1. Revisal of basics and robust regression Week 2: Model's marginal likelihood and Bayesian inference in the limit of large data Week 3: Probability as a measure of uncertainty and Decision theory Week 4: Score functions and model comparison Week 5: Model comparison and selection with cross validation and information criteria Week 6-7: Design of experiments

Lecture notes, Chosen sections from the Bayesian data analysis book, and articles to be announced during the course

Lectures, exercises and exercise groups

Exam. Course will be graded with grades 1-5. Exercises provide extra points to the exam.

Exam