You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: VBA_JensenShannon.m
    path of file: ./repos/VBA-toolbox/utils
    
    the code of the file until where you have to start completion: function [DJS,b,muy,Vy] = VBA_JensenShannon(mus,Qs,sources,w,base)
% evaluates the Jensen-Shannon divergence (DJS)
% function [DJS,b] = VBA_JensenShannon(mus,Qs,sources,w,base)
% This function evaluates the DJS:
% - either from a set of N-D Gaussian densities, in which case those are
% defined through their first- and second-order moments,
% - or binomial densities, in which case the first-order moments are
% sufficient.
% In addition, a set of individual weights for each of the n component
% densities should be provided.
% IN:
%   - mus: nx1 cell array of 1st-order moments
%   - Qs: nx1 cell array of 2nd-order moments
%   - sources: structure indicating distribution type
%   - w: nx1 vector of weights
%   - base: base for the log mapping ({'2'}, '10', or 'e')
% OUT:
%   - DJS: the Jensen-Shannon divergence
%   - b: the associated lower-bound on the ensuing probability of
%   classicafication error 
%   - muy/Vy: the 1st- and 2d- order moments of the Laplace approx to the
%   mixture
%
% /////////////////////////////////////////////////////////////////////////
%
% Given a set of probability distributions P_1, ..., P_n and weights $p$,
% the Jensen-Shannon Divergence JSD is:
%
% $$ 
%   JSD_p(P_1, ..., P_n) 
%     = H(\sum_{i=1}^n w_i P_i) - \sum_{i=1}^n w_i H(P_i) 
%     = Hy - sH

    
function [DJS,b,muy,Vy] = VBA_JensenShannon(mus,Qs,sources,w,base)
% evaluates the Jensen-Shannon divergence (DJS)
% function [DJS,b] = VBA_JensenShannon(mus,Qs,sources,w,base)
% This function evaluates the DJS:
% - either from a set of N-D Gaussian densities, in which case those are
% defined through their first- and second-order moments,
% - or binomial densities, in which case the first-order moments are
% sufficient.
% In addition, a set of individual weights for each of the n component
% densities should be provided.
% IN:
%   - mus: nx1 cell array of 1st-order moments
%   - Qs: nx1 cell array of 2nd-order moments
%   - sources: structure indicating distribution type
%   - w: nx1 vector of weights
%   - base: base for the log mapping ({'2'}, '10', or 'e')
% OUT:
%   - DJS: the Jensen-Shannon divergence
%   - b: the associated lower-bound on the ensuing probability of
%   classicafication error 
%   - muy/Vy: the 1st- and 2d- order moments of the Laplace approx to the
%   mixture
%
% /////////////////////////////////////////////////////////////////////////
%
% Given a set of probability distributions P_1, ..., P_n and weights $p$,
% the Jensen-Shannon Divergence JSD is:
%
% $$ 
%   JSD_p(P_1, ..., P_n) 
%     = H(\sum_{i=1}^n w_i P_i) - \sum_{i=1}^n w_i H(P_i) 
%     = Hy - sH
% $$
%
% where $H(P)=E[-log(P)]$ is the Shannon entropy for the distribution P 
%
% /////////////////////////////////////////////////////////////////////////

% _________________________________________________________________________
% check inputs

if ~exist('sources','var')
    sources = struct('type', 0); % default to gaussian
end