Instruction

Name Cr Method of study Time Location Organiser
Computational statistics I 5 Cr Course 7.1.2020 - 30.4.2020
Name Cr Method of study Time Location Organiser
Computational statistics I 5 Cr Lecture Course 2.9.2019 - 20.10.2019
Computational statistics I 5 Cr Course 1.6.2019 - 16.8.2019
Computational statistics I 5 Cr Other teaching 7.1.2019 - 2.5.2019
Computational statistics I 5 Cr Lecture Course 3.9.2018 - 21.10.2018
Computational statistics I 5 Cr Course 23.1.2018 - 11.5.2018
Computational statistics I 5 Cr Lecture Course 5.9.2017 - 23.10.2017

Target group

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.

Prerequisites

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)

Learning outcomes

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.

Timing

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.

Contents

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.

Completion

Exercises and home exam.

Activities and teaching methods in support of learning

Lectures, exercises, computer exercises, project work

Study materials

Lecture notes and articles to be announced during the course

Assessment practices and criteria

Computer excercises and a computer-based home exam, Course will be graded with grades 1-5

Recommended optional studies

Fundamentals of differential equations

Completion methods

Exercises and project work.