function [netv, restv] = kl_static_split(net, params, restv)% KL_STATIC  calculate batch-independent part of Kullback-Leibler divergence%%    Usage:%      [netv, restv] = kl_static_split(net, params, restv)%      where net (probdist struct) the found values for network,%      params (probdist struct) estimated values for variances of%      different values, hypers (probdist struct) estimated values for%      hyperparameters of the model and priors (probdist struct) user%      defined prior distributions for the hyperparameters.%      The returned value is split to parts dependent on the network%      weights in netv and independent of network weights restv.%      If restv is given as argument, it is not re-evaluated.% Copyright (C) 1999-2004 Antti Honkela, Harri Valpola,% and Xavier Giannakopoulos.%% This package comes with ABSOLUTELY NO WARRANTY; for details% see License.txt in the program package.  This is free software,% and you are welcome to redistribute it under certain conditions;% see License.txt for details.%% NOTE: Assume prior variance of net.w1 to be unityif nargin < 3,  restv = ...      kl_param(params.net.w2var, params.hyper.net.w2var.mean, ...	       params.hyper.net.w2var.var) + ...      kl_param(params.noise, params.hyper.noise.mean, ...	       params.hyper.noise.var) + ...      kl_param(params.src, params.hyper.src.mean, params.hyper.src.var) + ...      kl_param(params.hyper.net.w2var.mean, params.prior.net.w2var.mean.mean, ...	       params.prior.net.w2var.mean.var) + ...      kl_param(params.hyper.net.w2var.var, params.prior.net.w2var.var.mean, ...	       params.prior.net.w2var.var.var) + ...      kl_param(params.hyper.noise.mean, params.prior.noise.mean.mean, ...	       params.prior.noise.mean.var) + ...      kl_param(params.hyper.noise.var, params.prior.noise.var.mean, ...	       params.prior.noise.var.var) + ...      kl_param(params.hyper.src.mean, params.prior.src.mean.mean, ...	       params.prior.src.mean.var) + ...      kl_param(params.hyper.src.var, params.prior.src.var.mean, ...	       params.prior.src.var.var) + ...      kl_param(params.hyper.net.b1.mean, params.prior.net.b1.mean.mean, ...	       params.prior.net.b1.mean.var) + ...      kl_param(params.hyper.net.b1.var, params.prior.net.b1.var.mean, ...	       params.prior.net.b1.var.var) + ...      kl_param(params.hyper.net.b2.mean, params.prior.net.b2.mean.mean, ...	       params.prior.net.b2.mean.var) + ...      kl_param(params.hyper.net.b2.var, params.prior.net.b2.var.mean, ...	       params.prior.net.b2.var.var);endnetv = kl_param(net.w2, probdist(0), params.net.w2var, 1) + ...       kl_param(net.b1, params.hyper.net.b1.mean, params.hyper.net.b1.var) + ...       kl_param(net.b2, params.hyper.net.b2.mean, params.hyper.net.b2.var) + ...       kl_param(net.w1, probdist(0), probdist(0));