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3622347 Bayesian Inference 1, 5 Cp 
Code 3622347  Validity 01.01.1950 -
Name Bayesian Inference 1  Abbreviation BAY1 
Credits5 Cp  Date of expiry  
TypeIntermediate Studies Subject0390 Statistics 
ClassStudy Unit  Hours  
Study right   Grading0-5 
Recommended scheduling 
Organisation Computer Science (J,K) 

Learning outcomes 

The student understands the basics of Bayesian inference and is able to apply this knowledge to different inferential problems. Students are able to formulate a hierarchical Bayesian model for their own applications. Students are then able to perform inference on the model using their own data.


Starting from conditional probability, basic principles of Bayesian inference are derived Conjugate families of distributions and regression analysis are considered as examples. The use of Gibbs and Metropolis-Hastings sampling.

Modes of study 

Contact teaching includes lectures and exercises given from Joensuu. These are transmitted via video to Kuopio. Included into exercises is also one bigger task, where students will use their own data

Teaching methods 

Lectures (28 h), exercises (12 h), independent work (93 h)

Study materials 

Marin-Robert, Bayesian Core, A Practical Approach to Computational Bayesian Statistics, 2007.

Additional material: Bishop, Machine Learning and Pattern Recognition, 2007, Springer Verlag.  Murphy, Machine Learning a Probabilistic Perspective, 2012, The MIT Press.  Geisser, Predictive Inference: An Introduction, 2 painos, 1993, CRC press.

Evaluation criteria 

The grading scale of the course is 0-5.  It is based on the performance in the graded exercises.  Alternatively, the grade is based on the final exam only.


Probabilistic inference for data science 1 and 2, R-course, or corresponding knowledge


Autumn 2020

Offering data 

Course is open to anyone. No restrictions in the number of participants. Useful also for PhD students who need statistical methods in their research

Further information 

Teaching language: English


Letter (J, K) in front of the name of the course/exam indicates the campus on which teaching or exam takes place: J = Joensuu, K = Kuopio.

Present and future teaching
Functions Name Type Cp Teacher Timetable
Register (JOENSUU): Bayesian Inference 1  Lecture and exercise course  Ville Hautamäki  26.10.20 -29.01.21
Register (KUOPIO): Bayesian Inference 1  Lecture and exercise course  Ville Hautamäki  26.10.20 -29.01.21

Future exams
No exams