# lrozo/CollaborativeTransportation2D

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 function [BIC1, penalty] = BIC(data, model, L, N) % Leonel Rozo, 2015 % % This function computes the Bayesian Information Criterion (BIC) score for % a given model \lambda={\pi, \miu, \Sigma}. The model can be a TP-GMM or % some similar approaches (e.g., HMM). It outputs the BIC scores following % the Calinon formula. % % input: % matrix data Training data % structure model GMM model (priors, means and covariances) % float L Likelihood previously computed % Dimension of datapoints D = size(data, 1 ) ; % Number of gaussian components K K = model.nbStates ; % Number of frames P = model.nbFrames; %% * * * * * * * * * * Bayesian Information Criterion * * * * * * * * * * * % Computing number of free parameters required for a GMM of K components % assuming full covariance matrices Np = (K - 1) + K * P * (D + 0.5 * D * (D+1)) ; % Computing BIC's second term - Penalty factor penalty = 0.5 * (Np) * log(N); % Computing BIC_1 BIC1 = -L + penalty ;