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[ascl:1204.002] pyBLoCXS: Bayesian Low-Count X-ray Spectral analysis

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.

Code site:
http://hea-www.harvard.edu/AstroStat/pyBLoCXS/
Described in:
http://adsabs.harvard.edu/abs/2011ASPC..442..439S
Bibcode:
2012ascl.soft04002S

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ascl:1204.002
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