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pyBLoCXS: Bayesian Low-Count X-ray Spectral analysis

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pyBLoCXS: Bayesian Low-Count X-ray Spectral analysis

Postby owlice » Sun Apr 01, 2012 7:50 am

pyBLoCXS: Bayesian Low-Count X-ray Spectral analysis

Abstract: pyBLoCXS is a sophisticated Markov chain Monte Carlo (MCMC) based algorithm designed to carry out Bayesian Low-Count X-ray Spectral (BLoCXS) analysis in the Sherpa environment. The code is a Python extension to Sherpa that explores parameter space at a suspected minimum using a predefined Sherpa model to high-energy X-ray spectral data. pyBLoCXS includes a flexible definition of priors and allows for variations in the calibration information. It can be used to compute posterior predictive p-values for the likelihood ratio test. The pyBLoCXS code has been tested with a number of simple single-component spectral models; it should be used with great care in more complex settings.

Credit: Siemiginowska, Aneta; Kashyap, Vinay; Refsdal, Brian; van Dyk, David; Connors, Alanna; Park, Taeyoung

Site: http://hea-www.harvard.edu/AstroStat/pyBLoCXS/
http://adsabs.harvard.edu/abs/2011ASPC..442..439S

Bibcode: 2012ascl.soft04002S

ID: ascl:1204.002
Last edited by Ada Coda on Sat Dec 22, 2018 12:32 am, edited 1 time in total.
Reason: Updated code entry.
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