Opetus

Nimi Op Opiskelumuoto Aika Paikkakunta Järjestäjä
Computational statistics I 5 Cr Kurssi 1.6.2020 - 14.8.2020
Computational statistics I 5 Cr Luentokurssi 31.8.2020 - 18.10.2020
Nimi Op Opiskelumuoto Aika Paikkakunta Järjestäjä
Computational statistics I 5 Cr Kurssi 7.1.2020 - 30.4.2020
Computational statistics I 5 Cr Luentokurssi 2.9.2019 - 20.10.2019
Computational statistics I 5 Cr Kurssi 1.6.2019 - 16.8.2019
Computational statistics I 5 Cr Muu opetustapahtuma 7.1.2019 - 2.5.2019
Computational statistics I 5 Cr Luentokurssi 3.9.2018 - 21.10.2018
Computational statistics I 5 Cr Kurssi 23.1.2018 - 11.5.2018
Computational statistics I 5 Cr Luentokurssi 5.9.2017 - 23.10.2017

Kohderyhmä

The course is compulsory for students of the Statistics study track in the Master's Programme in Mathematics and Statistics.

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

The course belongs to the Statistics and Social statistics module in the Master's Programme in Mathematics and Statistics.

The course also belongs to the Machine Learning and Statistical Data Science modules in the Master's Programme in Data Science.

The course is available to students from other degree programmes.

Edeltävät opinnot tai edeltävä osaaminen

BSc courses on linear algebra, probability calculus, statistical
inference; basic programming skills; Bayesian inference (for example
MAT22005 Bayes-päättely, DATA11006 Statistical Data Science or
LSI35002 Bayesian inference in biosciences or similar)

Osaamistavoitteet

Knowledge and use of common general computational tools to perform reliable statistical analyses. Understanding the theoretical foundations of the most important Monte Carlo methods. Applying and implementing computational statistical procedures on a high-level programming language.

Ajoitus

Recommended time/stage of studies for completion: 1. or 2. year of MSc studies.

Term/teaching period when the course will be offered: yearly during the autumn term, period I.

Sisältö

Most important numerical and computational methods and principles for statistics. Theory and practice of methods for sampling from probability distributions including rejection sampling, importance sampling, generic Markov chain Monte Carlo and Hamiltonian Monte Carlo. Overview of modern methods for approximate inference. The computer projects can be implemented in Python (preferred) or R.

Suoritustavat

Exercises and home exam.

Oppimista tukevat aktiviteetit ja opetusmenetelmät

Lectures, exercises, computer exercises, project work

Oppimateriaali

Lecture notes and articles to be announced during the course

Arviointimenetelmät ja -kriteerit

Computer excercises and a computer-based home exam. The course will be graded with grades 1-5.

Suositeltavat valinnaiset opinnot

Fundamentals of differential equations

Toteutus

Exercises and project work.