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pyGTC: Parameter covariance plots

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Ada Coda
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pyGTC: Parameter covariance plots

Postby Ada Coda » Sat Jul 06, 2019 8:30 am

pyGTC: Parameter covariance plots

Abstract: pyGTC creates giant triangle confusogram (GTC) plots. Triangle plots display the results of a Monte-Carlo Markov Chain (MCMC) sampling or similar analysis. The recovered parameter constraints are displayed on a grid in which the diagonal shows the one-dimensional posteriors (and, optionally, priors) and the lower-left triangle shows the pairwise projections. Such plots are useful for seeing the parameter covariances along with the priors when fitting a model to data.

Credit: Bocquet, Sebastian; Carter, Faustin W.

Site: https://github.com/SebastianBocquet/pygtc
https://ui.adsabs.harvard.edu/abs/2017ApJ...837..124F

Bibcode: 2019ascl.soft07004B

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2016JOSS....1...46B

ID: ascl:1907.004

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