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ASCL Code Record

[ascl:1910.011] LEO-Py: Likelihood Estimation of Observational data with Python

LEO-Py uses a novel technique to compute the likelihood function for data sets with uncertain, missing, censored, and correlated values. It uses Gaussian copulas to decouple the correlation structure of variables and their marginal distributions to compute likelihood functions, thus mitigating inconsistent parameter estimates and accounting for non-normal distributions in variables of interest or their errors.

Code site:
https://github.com/rfeldmann/leopy
Described in:
https://ui.adsabs.harvard.edu/abs/2019A%26C....2900331F
Bibcode:
2019ascl.soft10011F

Views: 101

ascl:1910.011
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