The course will be evaluated based on weekly computer exercises returned to Moodle (50% of the grade) and a home exam (50% of the grade).
You need to obtain at least 50% of maximum points from the weekly computer exercises in order to participate at the exam.
After the opening session on Tuesday 3 September, the weekly course schedule will consist of interactive lectures with small computer programming tasks on Thursday and Friday. The Tuesday session will be a workshop for working on the weekly computer exercises, the deadline for which will be on Wednesday.
There is no textbook, but lecture notes covering the course contents will be provided.
Home exam problems will be released on Friday 18 October. The deadline for returning your solutions will be on Friday 1 November.
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.
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)
Fundamentals of differential equations
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.
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.
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.
Exercises and home exam.
Lecture notes and articles to be announced during the course
Lectures, exercises, computer exercises, project work
Computer excercises and a computer-based home exam. The course will be graded with grades 1-5.
Exercises and project work.