The sherpa.sim.mh module

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 (see Protassov et al., 2002, ApJ, 571, 545). Future versions will allow for the incorporation of calibration uncertainty (Lee et al., 2011, ApJ, 731, 126).

MCMC is a complex computational technique that requires some sophistication on the part of its users to ensure that it both converges and explores the posterior distribution properly. 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. Readers interested in Bayesian low-count spectral analysis should consult van Dyk et al. (2001, ApJ, 548, 224). pyBLoCXS is based on the methods in van Dyk et al. (2001) but employs a different MCMC sampler than is described in that article. In particular, pyBLoCXS has two sampling modules. The first uses a Metropolis-Hastings jumping rule that is a multivariate t-distribution with user specified degrees of freedom centered on the best spectral fit and with multivariate scale determined by the Sherpa function, covar(), applied to the best fit. The second module mixes this Metropolis Hastings jumping rule with a Metropolis jumping rule centered at the current draw, also sampling according to a t-distribution with user specified degrees of freedom and multivariate scale determined by a user specified scalar multiple of covar() applied to the best fit.

A general description of the MCMC techniques we employ along with their convergence diagnostics can be found in Appendices A.2 - A.4 of van Dyk et al. (2001) and in more detail in Chapter 11 of Gelman, Carlin, Stern, and Rubin (Bayesian Data Analysis, 2nd Edition, 2004, Chapman & Hall/CRC).

http://hea-www.harvard.edu/AstroStat/pyBLoCXS/

Classes

LimitError
MH(fcn, sigma, mu, dof, *args[, rng])	The Metropolis Hastings Sampler
MetropolisMH(fcn, sigma, mu, dof, *args[, rng])	The Metropolis Metropolis-Hastings Sampler
Sampler()
Walk([sampler, niter])

Functions

dmvnorm(x, mu, sigma[, log])	Probability Density of a multi-variate Normal distribution
dmvt(x, mu, sigma, dof[, log, norm])	Probability Density of a multi-variate Student's t distribution

Class Inheritance Diagram