%> @file mexEpdf.m %> @brief File mapping root class of BM from BDM % ====================================================================== %> @brief Abstract class of Bayesian Model (estimator), bdm::BM % %> This class provides a bridge between bdm::BM and Matlab % ====================================================================== classdef mexBM properties %> description of internal variables of parameters rv=RV %> description of internal variables of data in condition rvc=RV %> description of internal variables of observed data rvy=RV %> log of evidence (marginal likelihood) potentially computed by the % bayes rule for one step log_evidence %> posterior density - offspring of mexEpdf! apost_pdf % end methods %> check consistency of the object and fill defaults function p=validate(p) % checks if all paramateres match end %> dimensionality of the class: dims = [size_of_posterior size_of_data size_of_condition] function dims=dimensions(p) %please fill %dims = [size_of_posterior size_of_data size_of_condition] dims = [0,0,0] % end %> Performs Bayesian update of the internal posterior using data \a dt and condition \a cond. function obj=bayes(obj,dt,cond) % transform old estimate into new estimate end %> Computes predictor of the observed data in the next step function p=epredictor(obj,cond) % return posterior density end %%%%%%%% default functions %%%%%%%%%%%%%% function r = get_rv(obj) r=obj.rv; end function r = get_rvc(obj) r=obj.rvc; end function r = get_rvy(obj) r=obj.rvy; end %> Evidence of the last data, \f[f(y_t|y_0\ldots y_t-1, cond_0\ldots cond_t\f] function ev = logevidence(obj) ev = obj.log_evidence; end %> Posterior pdf function post=posterior(obj); post = obj.apost_pdf; end end end