Organised by:
in collaboration with:



social media:


Accesso Utenti

Heidelberg Institute for Theoretical Studies
Miscellaneous Information:

Abstract Reference: 30927
Identifier: P2.16
Presentation: Poster presentation
Key Theme: 2 Management of Scientific and Data Analysis Projects

Probability Density Functions for Astronomy

Polsterer Kai L.

In many applications in astronomy, uncertainty quantification plays an important role. Probability density functions allow to quantify the likelihood of certain results and therefore enable scientist to produce better analysis results. We present a Python package to generate PDFs for classification and regression tasks. Besides providing several functionalities to generate such PDFs, we present a whole tool set for evaluating the quality and visualizing the performance of the generated PDFs.