mcmc.goodman_weare

Usage

obj = mcmc;
xPosterior = obj.goodman_weare(y,x0,weights,fitting,modelFWD,varargin)

Input	Description
y	measurements, [Nmeas, Nvoxels]
x0	structure variable containing starting points of all model parameters to be estimated
weights	N-D wieghts, same dimension as ‘data’ (optional)
fitting	structure contains fitting algorithm parameters
fitting.model_params	1xM cell variable, name of the model parameters, e.g. {‘S0’,’R2star’,’noise’};
fitting.lb	1xM numeric variable, fitting lower bound, same order as field ‘model_params’, e.g. [0.5, 0, 0.001];
fitting.ub	1xM numeric variable, fitting upper bound, same order as field ‘model_params’, e.g. [2, 1, 0.1];
fitting.iteration	# MCMC iterations
fitting.repetition	# repetition of MCMC proposal
fitting.thinning	sampling interval between iterations
fitting.burnin	iterations to be discarded at the beginning, if >1, the exact number will be used; else iteration*burnin
fitting.StepSize	step size for ‘GW’ in MCMC proposal (‘GW’ only)
fitting.Nwalker	# random walkers (‘GW’ only)
FWDfunc	function handle for forward signal generation; size of the output must match size of ‘data’
varargin	additional input for FWDfunc other than ‘parameter’ and ‘mask’ (same order as FWDfunc)

Output	Description
xPosterior	structure contains MCMC posterior samples
xPosterior.(model_params{k})	Model parameter MCMC posterior samples

Note

‘noise’ is always required in fitting.model_params.