Workshop content

Dealing with uncertainty is a fundamental component of climate science. Typical examples where uncertainty is present include estimating correlation coefficients or linear regression parameters when developing simple models of physical processes. Bayesian methods are attractive for addressing this uncertainty as they provide probabilities for the parameters of interest rather than point estimates.

Recent developments in theory and open source software mean that Bayesian methods are now effective tools for climate scientists. In this workshop we will cover the basic theory behind Bayesian methods and then work through some familiar analysis such as correlation and linear regression from a Bayesian perspective. The workshop will demonstrate use of interactive notebooks using python or R.  

Notify participation

OBS! Places are limited, so please contact Liam Brannigan if you wish to participate.

Time and Place

Friday February 2nd 2017, 11.30-12.30, 13.30-15.30
Kuling C502a, Arrhenius Laboratory, 6th floor

More information

For more information, contact Liam Brannigan.