FABADA is a fitting algorithm for Bayesian data analysis, the theory of which is detailed here: http://dx.doi.org/10.1088/1742-6596/325/1/012006
This documentation covers details of it’s implementation in Mantid.
Currently in order to use FABADA the cost function must be set to Least Squares.
The values for ChainLength and MaxIterations should be set to relatively high values where (e.g. MaxIterations = 20^6 and ChainLength = 10^6).
Currently the starting values used for each parameter are required to be a fairly good estimate of the actual value, in the event that a parameter is not estimated to sufficient accuracy convergence of the parameters will not be reached and an error message will inform of the unconverged parameters.
Example: A simple example
ws_data = Load(Filename='irs26176_graphite002_red.nxs')
ws_res = Load(Filename='irs26173_graphite002_res.nxs')
function_str = 'composite=Convolution,FixResolution=tue,NumDeriv=false;name=Resolution,Workspace=ws_res,WorkspaceIndex=0;(composite=CompositeFunction,NumDeriv=true;name=Lorentzian,Amplitude=1,PeakCentre=0.01,FWHM=0.5;name=Lorentzian,Amplitude=1,PeakCentre=0.01,FWHM=0.5)'
minimizer_str = "FABADA,Chain Lengh=1000000,Steps between values=10,Convergence Criteria=0.01,PDF=pdf,Chains=chain,Converged chain=conv,Cost Function Table=CostFunction,Parameter Erros =Errors"
Fit(Function = function_str,InputWorkspace=ws_data,WorkspaceIndex=3,StartX=-0.25,EndX=0.25,CreateOutput=True,Output = 'result',OutputCompositeMembers=True,MaxIterations=2000000, Minimizer=minimizer_str)
Category: Concepts