Course

Published 4.4.2025

(Updated 25.7.2025)

Helsingin yliopisto

Bayesian Data Analysis

Bayesian Data Analysis -course at University of Helsinki

29.9.2025 - 1.3.2026

5 cr

Face-to-face

Advanced studies

The course starts with introduction to Bayesian inference. We will study Bayes theorem and its components: prior distribution and likelihood function and how these define the posterior distribution. We will apply the Bayes theorem to inference on population parameters using Binomial model.

After this we move on to technical necessities related to Bayesian inference. The main mathematical operation in Bayesian analysis is integration which is used when solving for the posterior distribution, in marginalization over model parameters and in prediction. In this course, we learn how to use Markov chain Monte Carlo (MCMC) methods to approximate the required integrals. We will use R and Stan software to conduct the practical calculations in all exercises.

Next, we will study (generalized) linear models and few common hierarchical parametric models (Binomial, Gaussian, Poisson) and develop practical experience on their use in some common applied questions.

For last we will introduce model assessment and criticism with posterior predictive checks and sensitivity analyses and take a quick look to more fundamental topics in Bayesian statistics including exchangeability and conditional independence, graphical models and model comparison. However, more thorough treatment of these topics is left for course MAST32004 Advanced Bayesian Inference.

Additional information

Bayesian data analysis, Lähiopetus 27.10.–15.12.2025

How will you study

Additional information and registration

Price and registration period

Check on the university's website

Go to the institution's website(Opens in a new tab)

Additional information:

Dates

29.9.2025 - 1.3.2026

Fields

Natural sciences

Scope

5 cr

1 component

Code

LSI35002

What you need to know before the course

Basics in statistics and probability calculus. Students should know how to calculate ordinary probabilities, conditional probabilities and marginal probabilities and how to use sum and product rule. Furthermore, students should know the difference between discrete distribution and continuous distribution and how to calculate expectation/mean, variance, standard deviation and p-fractiles for both kind of distributions. Familiarity in basic distributions like Binomial distribution, Poisson distribution, Normal distribution and Beta distribution is benefitial.

What you will learn

The course covers the basic theory behind Bayesian statistical inference and its applications to common problems in environmental and biological sciences. After the course students understand the Bayes theorem and the related concepts, including prior, posterior and predictive distribution and likelihood function. Students will also be familiar with graphical model representation and basics in model assessment and criticism. Students are also able to apply Bayes theorem to write down simple hierarchical Bayesian models for common data analysis problems such as basic parametric models and (generalized) linear regression. Students will be familiar with the basic concept of Markov chain Monte Carlo (MCMC) and are able to apply MCMC methods to solve hierarchical Bayesian models using the R and Stan software.

Learning material

Additional information

Completion methods

Completing the course requires passing both the course exercises and the course exam.

This course can be arranged in a different format and with different assessment methods for Open University students.

Assessment practices and criteria

The exercises and the exam are graded 0-5 and the course grade is their average.

Activities and methods in support of learning

Lectures and exercise classes.

Students are required to read course material, complete required proportion of the exercises and pass the exam
Teacher and course assistants will assist during the lectures, exercises and via Moodle

Target groups

The Master's Programme in Life Science Informatics (LSI) and The master's Programme in Data Science (DATA).

The course is also available to students from other degree programmes and recommended especially for students from:

Doctoral Programme in Interdisciplinary Environmental Sciences (Denvi)
Doctoral Programme in Wildlife Biology (Luova)
Master's programme in Ecology and Evolutionary Biology (EEB)

Teaching period when the course will be offered

The course is offered every year in the 2nd period.

Recommended time or stage of studies for completion

LSI, DATA: Recommended time for completion is during the first year of masters studies

DENVI,LUOVA: Any time during the studies

EEB: Second year of studies after completing EEB-003 Statistical Methods in Ecology

The recommended courses after this course are MAST32004 Advanced Bayesian Inference, MAST32005 Spatial Modelling and Bayesian Inference and MAST32001 Computational statistics

Study module

Ecological Informatics and Biostatistics study tracks of LSI.

Expiry of studies

In accordance with University guidelines.

Language of instruction

English

EQF levelMaster's / EQF level 7

Organiser

Helsingin yliopisto

Contact details

avoin-student@helsinki.fi

Belongs to themes:

Change management and analytics