set_sampler_opt¶
-
sherpa.ui.set_sampler_opt(opt, value)¶ Set an option for the current MCMC sampler.
Parameters: - opt (str) – The option to change. Use get_sampler to view the available options for the current sampler.
- value – The value for the option.
See also
get_sampler()- Return the current MCMC sampler options.
set_prior()- Set the prior function to use with a parameter.
set_sampler()- Set the MCMC sampler.
Notes
The options depend on the sampler. The options include:
- defaultprior
- Set to
Falsewhen the default prior (flat, between the parameter’s soft limits) should not be used. Use set_prior to set the form of the prior for each parameter. - inv
- A bool, or array of bools, to indicate which parameter is on the inverse scale.
- log
- A bool, or array of bools, to indicate which parameter is on the logarithm (natural log) scale.
- original
- A bool, or array of bools, to indicate which parameter is on the original scale.
- p_M
- The proportion of jumps generatd by the Metropolis jumping rule.
- priorshape
- An array of bools indicating which parameters have a user-defined prior functions set with set_prior.
- scale
- Multiply the output of covar by this factor and use the result as the scale of the t-distribution.
Examples
>>> set_sampler_opt('scale', 3)