MH

class sherpa.sim.mh.MH(fcn, sigma, mu, dof, *args)

Bases: sherpa.sim.mh.Sampler

The Metropolis Hastings Sampler

Methods Summary

accept(current, current_stat, proposal, ...)	Should the proposal be accepted (using the Cash statistic and the t distribution)?
accept_mh(current, current_stat, proposal, ...)
calc_fit_stat(proposed_params)
calc_stat(proposed_params)
dmvt(x[, log, norm])
draw(current)	Create a new set of parameter values using the t distribution.
init([log, inv, defaultprior, priorshape, ...])
mh(current)	MH jumping rule
reject()
tear_down()
update(stat, mu[, init])	include prior

Methods Documentation

accept(current, current_stat, proposal, proposal_stat, **kwargs)

Should the proposal be accepted (using the Cash statistic and the t distribution)?

accept_mh(current, current_stat, proposal, proposal_stat)

calc_fit_stat(proposed_params)

calc_stat(proposed_params)

dmvt(x, log=True, norm=False)

draw(current)

Create a new set of parameter values using the t distribution.

Given the best-guess (mu) and current (current) set of parameters, along with the covariance matrix (sigma), return a new set of parameters.

init(log=False, inv=False, defaultprior=True, priorshape=False, priors=(), originalscale=True, scale=1, sigma_m=False)

mh(current)

MH jumping rule

reject()

tear_down()

update(stat, mu, init=True)

include prior