set_sampler_opt

Synopsis

Define sampler options for a pyBLoCXS chain.

Syntax

set_sampler_opt(optname, val )

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_opt command may be used to define a particular sampler option for a pyBLoCXS chain; the sampler itself may be set with set_sampler ("ahelp set_sampler").

optname - sampler option name
val - value for the option

Available samplers and their associated configuration options are returned by the list_samplers and get_sampler commands, respectively. ("ahelp list_samplers" and "ahelp get_sampler").

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

Example 1

sherpa> set_sampler("MH")
sherpa> set_sampler_opt("scale", 0.5)

Set the current sampler to Metropolis-Hastings, and its "scale" configuration option to a value of 0.5 ("scale" refers to the scalar multiple of the output of covar() used in the scale of the t-distribution.).

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

sherpa> set_sampler("MH")
sherpa> set_sampler_opt("scale", 0.5)

Example 2

sherpa> set_sampler("MetropolisMH")
sherpa> set_sampler_opt("p_M", 0.33)

Set the current sampler to Metropolis with Metropolis-Hastings, and its "p_M" configuration option to a value of 0.33 ("p_M" refers to the proportion of jumps generated by the Metropolis jumping rule).

Bugs

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