set_sampler

sherpa.ui.set_sampler(sampler)

Set the MCMC sampler.

The sampler determines the type of jumping rule to be used when running the MCMC analysis.

Parameters:sampler (str or sherpa.sim.Sampler instance) – When a string, the name of the sampler to use (case insensitive). The supported options are given by the list_samplers function.

See also

get_draws()

Run the pyBLoCXS MCMC algorithm.

list_samplers()

List the MCMC samplers.

set_sampler()

Set the MCMC sampler.

set_sampler_opt()

Set an option for the current MCMC sampler.

Notes

The jumping rules are:

MH

The Metropolis-Hastings rule, which jumps from the best-fit location, even if the previous iteration had moved away from it.

MetropolisMH

This is the Metropolis with Metropolis-Hastings algorithm, that jumps from the best-fit with probability p_M, otherwise it jumps from the last accepted jump. The value of p_M can be changed using set_sampler_opt.

PragBayes

This is used when the 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 (the nsubiters option) MCMC sub-iteration steps are carried out, choosing between Metropolis and Metropolis-Hastings types of samplers with probability p_M. Only the last of these sub-iterations are kept in the chain. The nsubiters and p_M values can be changed using set_sampler_opt.

FullBayes

Another sampler for use when including uncertainties due to the effective area.

Examples

>>> set_sampler('metropolismh')