mcmc.rst		mcmc.rst
	skipping to change at line 21		skipping to change at line 21
	- emcee_walkers: emcee v3.x multi-Walkers algorithms with M oves		- emcee_walkers: emcee v3.x multi-Walkers algorithms with M oves
	- dynesty: unsupported		- dynesty: unsupported
	- multinest: still experimental		- multinest: still experimental
	- pymc3: to be implemented		- pymc3: to be implemented
	mcmc_sampling:		mcmc_sampling:
	- galpak: 'Cauchy' [default] | 'Normal' | 'AdaptiveCauchy'		- galpak: 'Cauchy' [default] | 'Normal' | 'AdaptiveCauchy'
	- emcee_walkers: 'walkers' [default] | 'walkersCauchy' | 'D E' | 'Snooker' | 'Cauchy' | 'Normal'		- emcee_walkers: 'walkers' [default] | 'walkersCauchy' | 'D E' | 'Snooker' | 'Cauchy' | 'Normal'
	- multinest: None		- multinest: None
	- pymc3: to be implemented		- pymc3: to be implemented
	The proposal sampling methods		The proposal sampling methods
	.. figure:: images/GalPaK_MCMC.png		.. figure:: images/GalPaK_MCMC.png
	:scale: 60%		:scale: 60%
	:align: center		:align: center
	GalPaK3D uses an internal :class:`MCMC <galpak.MCMC>` class which extend th e galpak class.		GalPaK3D uses an internal :class:`MCMC <galpak.MCMC>` class which extend th e galpak class.
	This can be used to call its likelihood such as : ::		This can be used to call its likelihood such as: ::
	li = gk(params)		li = gk(params)
	using a :meth:`self.__call__() <MCMC.__call__>` method which returns the ln prob		using a :meth:`self.__call__() <MCMC.__call__>` method which returns the ln prob
	Here is a full example: ::		Here is a full example: ::
	import galpak		import galpak
	gk=galpak.GalPaK3D('data/input/GalPaK_cube_1101_from_paper.fits',model= galpak.DefaultModel())		gk=galpak.GalPaK3D('data/input/GalPaK_cube_1101_from_paper.fits',model= galpak.DefaultModel())
	p=gk.model.Parameters()		p=gk.model.Parameters()
	params=p.from_ndarray([15,15,15,1e-16,5,60,90,1,100,10])		params=p.from_ndarray([15,15,15,1e-16,5,60,90,1,100,10])
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