Last modified: December 2013

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AHELP for CIAO 4.11 Sherpa v1


Context: statistics


List all available pyBLoCXS samplers.




The Sherpa list_samplers command returns the list of available samplers, or jumping rules, available for use in the pyBLoCXS algorithm.

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 - i.e. after a standard Sherpa fit.

Jumping Rules

"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.)

The get_sampler* and set_sampler* functions are available for accessing and modifying the attributes of each sampler type; see the corresponding ahelp files for details (e.g., "ahelp get_sampler").

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


sherpa> list_samplers()

Return the names of the available jumping rules for use in the algorithm.

sherpa> list_samplers()
['MetropolisMH', 'MH', 'PragBayes']


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

See Also

list_functions, list_response_ids
list_iter_methods, list_methods
list_model_components, list_models