classdef mexLaplaceBM < mexBM % Approximate Bayesian estimator of parameters of Laplace distributed observation. % Maximum likelihood approximation of the Bayes rule is used, posterior is in the form of dirac. properties max_window_length = 10; % max window length data_window =[]; % estimate of mu is median of observations end methods function obj=validate(obj) % prepare all internal objects for use obj.apost_pdf = mexDirac; obj.apost_pdf.point = [0;0]; obj.log_evidence = 0; % evidence is not computed! disp('val'); end function dims=dimensions(obj) %please fill %dims = [size_of_posterior size_of_data size_of_condition] dims = [2,1,0] % we have: [2d parameters, 1d observations, 0d condition] end function obj=bayes(obj,dt,cond) if size(obj.data_window,2)>=obj.max_window_length obj.data_window = [dt obj.data_window(1:end-1)]; else obj.data_window = [dt obj.data_window]; end % transform old estimate into new estimate m_hat = mean(obj.data_window); b_hat = sum(abs(obj.data_window-m_hat))/ size(obj.data_window,2); obj.apost_pdf.point = [m_hat; b_hat]; end function p=epredictor(obj,cond) % return predictive density (max likelihood) p = mexLaplace; p.mu = obj.apost_pdf.point(1); p.b = obj.apost_pdf.point(2); p=p.validate; end end end