# Robert Feldmann

## SNSF Professor | Institute for Computational Science, University of Zurich

### Scripts

- Fitting SFR distributions (incl. zero-inflated negative binomials), see Feldmann 2017: R script, catalog converter

### LEO-Py

Incomplete and uncertain observational data, such as those arising from detection limits and measurement uncertainties, are commonplace in astronomy and astrophysics. Unfortunately, many standard techniques are not well equipped to deal with such data sets potentially resulting in strongly biased or even inconsistent estimates of the underlying model parameters.
*The Likelihood Estimator for Observational data with Python* employs Gaussian copulas to decouple the correlation structure of variables and their marginal distributions resulting in a flexible method to compute likelihood functions of data in the presence of measurement uncertainty, censoring, and missing data.
LEO-Py is available as an open-source Python package on GitHub and the Python Package Index.

### ZEBRA

*The Zurich Extragalactic Bayesian Redshift Analyzer* is a publicly available, open source software package that computes photometric redshifts with outstanding accuracy. A detailed description of the approach adopted by ZEBRA can be found in Feldmann et al. 2006. You can clone ZEBRA directly from its bit-bucket repository.