Kaisa_2012_3_photo by Veikko Somerpuro

The course covers the basic theory behind Bayesian modelling and its application to common problems in biosciences.

Lectures: Monday (Infokeskus, atk 138) and Tuesday (Biokeskus 3, sali 6602)
Exercises (Ryhmä 1): Thursdays (Infokeskus, atk 138).

Course communication will happen in Moodle.

Enrol
2.10.2017 at 09:00 - 14.12.2017 at 23:59
Moodle
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Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Mon 30.10.2017
14:15 - 16:00
Tue 31.10.2017
14:15 - 16:00
Mon 6.11.2017
14:15 - 16:00
Tue 7.11.2017
14:15 - 16:00
Mon 13.11.2017
14:15 - 16:00
Tue 14.11.2017
14:15 - 16:00
Mon 20.11.2017
14:15 - 16:00
Tue 21.11.2017
14:15 - 16:00
Mon 27.11.2017
14:15 - 16:00
Tue 28.11.2017
14:15 - 16:00
Mon 4.12.2017
14:15 - 16:00
Tue 5.12.2017
14:15 - 16:00
Mon 11.12.2017
14:15 - 16:00
Tue 12.12.2017
14:15 - 16:00

Other teaching

02.11. - 14.12.2017 Thu 10.15-12.00
Jarno Vanhatalo
Teaching language: English

Description

Master's Programme in Life Science Informatics is responsible for the course.

Module where the course belongs to:

  • Eco-evolutionary informatics

The course is available to students from other degree programmes. The

course is recommended especially for students in

* Doc­toral Pro­gramme in In­ter­dis­cip­lin­ary En­vir­on­mental Sci­ences (Denvi)

* Doc­toral Pro­gramme in Wild­life Bio­logy (Luova)

* Datascience Master's programme

Basics in statistics and probability calculus.

After the course students are recommended to continue to:

MAST32004, Advanced course in Bayesian statistics, 5 cr LSI35003, Project work in eco-evolutionary informatics, 5 cr 57429, Spatial modelling and Bayesian inference, 5 cr

The course covers the basic theory behind probabilistic and Bayesian modelling and their applications to common problems in environmental and biological sciences. After the The course students understand the Bayes rule 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, criticism and comparison. Students are also able to apply Bayes rule to construct simple hierarchical Bayesian models. Students are 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 JAGS/STAN software.

Recommended time for completion is during the first year of masters studies or the first year of PhD studies.

The course is offered in 2nd period.

  • Part 1: Introduction to Bayesian inference: Bayes rule, prior and posterior distribution, likelihood function, Binomial model, mark-recapture analysis
  • Part 2: Technical necessities and few practical models: Monte Carlo methods, Markov chain Monte Carlo (MCMC), marginalization, prediction, JAGS software, linear and generalized linear models
  • Part 3: Hierarchical models and Probability fundamentals: exchangeability, conditional independence, graphical models, hierarchical Binomial and Gaussian models
  • Part 4: Model assessment, criticism and comparison: posterior predictive check, sensitivity analysis, posterior predictive comparison, cross-validation, Bayes Factor
  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson D. B., Vehtari A. and Rubin, D. B. (2013). Bayesian Data Analysis. Chapman & Hall/CRC. Second or third edition.
  • Number of selected articles
  • R programming environment and JAGS/STAN software

The book is read partially (chapters to be announced during the course).

The articles need to be read fully.

Students are required to read course material, complete at least 50% of the exercises and pass the exam. Lectures are interactive and teacher will assist with exercises during the lectures and via Moodle between the lectures. Additionally, there is exercise group once a week where students can get help for exercises.

The course grade will be 0.5*[grade from the exercises] + 0.5*[grade from the exam]

The course consists of lectures, exercises and an exam. Completion requires solving at least 50% of the exercises and passing the exam.