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LEO-Py: Likelihood Estimation of Observational data with Python

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Ada Coda
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LEO-Py: Likelihood Estimation of Observational data with Python

Postby Ada Coda » Wed Oct 30, 2019 1:49 am

LEO-Py: Likelihood Estimation of Observational data with Python

Abstract: 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.

Credit: Feldmann, R.

Site: https://github.com/rfeldmann/leopy
https://ui.adsabs.harvard.edu/abs/2019A%26C....2900331F

Bibcode: 2019ascl.soft10011F

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2019A%26C....2900331F

ID: ascl:1910.011

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