HOMER: MCMC-based inverse modeling code

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Ada Coda
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HOMER: A Bayesian inverse modeling code

Post by Ada Coda » Wed Apr 01, 2020 1:51 am

HOMER: A Bayesian inverse modeling code

Abstract: HOMER (Helper Of My Eternal Retrievals) is a machine-learning-accelerated Bayesian inverse modeling code. Given some data and uncertainties, the code determines the posterior distribution of a model. HOMER uses MC<sup>3</sup> (ascl:1610.013) for its MCMC; its forward model is a neural network surrogate model trained by MARGE (ascl:2003.010). The code produces plots of the 1D marginalized posteriors, 2D pairwise posteriors, and parameter history traces, and can also overplot the 1D and 2D posteriors for comparison with another posterior. HOMER computes the Bhattacharyya coefficient to compare the similarity of two 1D marginalized posteriors.

Credit: Himes, Michael D.; Wright, David C.; Scheffer, Zaccheus; Harrington, Joseph

Site: https://github.com/exosports/homer
https://ui.adsabs.harvard.edu/abs/2020arXiv200302430H

Bibcode: 2020ascl.soft03011H

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2020arXiv200302430H

ID: ascl:2003.011
Last edited by Ada Coda on Thu Apr 09, 2020 7:05 pm, edited 1 time in total.
Reason: Updated code entry.

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