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[ascl:1907.004] pyGTC: Parameter covariance plots

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.

Code site:
https://github.com/SebastianBocquet/pygtc
Used in:
https://ui.adsabs.harvard.edu/abs/2017ApJ...837..124F
Described in:
https://ui.adsabs.harvard.edu/abs/2016JOSS....1...46B
Bibcode:
2019ascl.soft07004B

Views: 520

ascl:1907.004
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