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This course aims to provide solid foundations to implementing linear regression, three types of Generalised Linear Models, and hierarchical models.

COURSE DETAILS

Stan is a powerful tool for performing inference for systems encountered in the sciences and social sciences and, importantly, it has an active user/developer community which can help you when you get stuck.

In this session, we will introduce users to Stan and show how to perform inference for a variety of models, including discrete parameter models, hierarchical models and differential equations. 

LEARNING OUTCOMES

By the end of the session participants will be able to:
• Develop a basic understanding of the open-source ‘Stan’ probabilistic programming language.
• Apply Bayesian inference to research problems, including their own.

 INTENDED FOR

 All

Additional Information

COURSE PRE-REQUISITE
Ideally, some basic knowledge of Bayesian statistics obtained, for example, through the introduction course.

Also, some knowledge of a mathematical programming language, for example: R, Matlab, Python, Mathematica, or C++.


PARTICIPANTS WILL NEED TO BRING LAPTOPS.

NUMBER OF PLACES

 30

COURSE LEADER

Ben Lambert

TIME / Date

09.30 - 17.00 | 29th May 2024 

BOOKING INFORMATION

Book your place on Applying Bayesian Statistics using Stan by clicking here.

TERMS & CONDITIONS

When registering for this course, please check our Terms and Conditions.