set_sampler

Synopsis

Set the current pyBLoCXS sampler.

set_sampler(name)

Description

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 algorithm explores parameter space at a suspected minimum using a predefined Sherpa model to high-energy X-ray spectral data.

The Sherpa set_sampler command may be used to define the current pyBLoCXS sampler (the type of jumping rule to be used in MCMC) to be either "MH" or "MetropolisMH". "MH" is Metropolis-Hastings, which always jumps from the best-fit, and "MetropolisMH" is Metropolis with Metropolis-Hastings that jumps from the best-fit with probability 'p_M', else it jumps from the last accepted jump. "PragBayes" is used when effective area calibration uncertainty is to be included in the calculation. (At each nominal MCMC iteration, a new calibration product is generated, and a series of N (option in set_sampler_opt) MCMC sub-iteration steps are carried out, choosing between Metropolis and Metropolis-Hastings types of samplers with probability p_M (option in set_sampler_opt). Only the last of these sub-iterations are kept in the chain.)

• name - the name of the sampler, or jumping rule to be used in MCMC

Available samplers are returned by the list_samplers command ("ahelp list_samplers"). The configuration options for each pyBLoCXS sampler type may be viewed and customized using the get_sampler commands ("ahelp get_sampler").

Refer to the pyBLoCXS documentation for additional information about the algorithm.

Example 1

sherpa> set_sampler("MetropolisMH")

Set the current MCMC jumping rule to a mix of Metropolis and Metropolis-Hastings.

sherpa> load_pha("pha.fits")
sherpa> set_model(xsphabs.abs1*powlaw1d.p1)
sherpa> set_stat("cash")
sherpa> fit()
sherpa> covar()

sherpa> print get_sampler_name()
        MH
sherpa> set_sampler("MetropolisMH")

Example 2

sherpa> set_sampler("MH")

Set the current MCMC jumping rule to Metropolis-Hastings.

sherpa> load_pha("pha.fits")
sherpa> set_model(xsphabs.abs1*powlaw1d.p1)
sherpa> set_stat("cash")
sherpa> fit()
sherpa> covar()

sherpa> print get_sampler_name()
        MetropolisMH

sherpa> set_sampler("MH")

Bugs

See the bugs pages on the Sherpa website for an up-to-date listing of known bugs.

See Also

confidence

set_conf_opt, set_covar_opt, set_proj_opt

data

set_areascal, set_arf, set_backscal, set_bkg, set_coord, set_counts, set_data, set_exposure, set_grouping, set_quality, set_rmf, set_staterror, set_syserror

filtering

set_filter

info

list_stats

methods

set_iter_method, set_iter_method_opt, set_method, set_method_opt

modeling

get_par, set_bkg_model, set_full_model, set_par, set_pileup_model, set_source, set_xsabund, set_xscosmo, set_xsxsect, set_xsxset

statistics

cash, chi2constvar, chi2datavar, chi2gehrels, chi2modvar, chi2xspecvar, chisquare, cstat, get_prior, leastsq, list_priors, set_prior, set_sampler_opt, set_stat

utilities

set_analysis, set_default_id

visualization

image_setregion