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: VB_CRP.m
    path of file: ./repos/VBA-toolbox/modules/classification/CRP
    
    the code of the file until where you have to start completion: function [post,out] = VB_CRP(y,alpha,options)
% function [post,out] = VB_CRP(y,alpha,options)
% VB scheme for the CRP-MoG classifier.
% IN:
%   - y : the pxn data matrix, where n is the number of
%   'observations' to be classified, and p is the dimension of each of
%   these observations
%   - alpha : CRP concentration parameter
%   - options : a structure containing infos regarding initialization and
%   inversion scheme (can be left empty -> defaults):
%       .priors : structure of priors for generative density
%           .mu0: px1 prior expectation of Gaussian component mean
%           .S0: prior covariance matrix of Gaussian component mean
%           .a0: prior counts for each class
%           .b0: prior sum of squared error within each class
%           NB: a0./b0 is the a priori expected precision (inverse
%           variance) of Gaussian components.
%       .TolFun = min increment of the free energy {1e-3}

    
function [post,out] = VB_CRP(y,alpha,options)
% function [post,out] = VB_CRP(y,alpha,options)
% VB scheme for the CRP-MoG classifier.
% IN:
%   - y : the pxn data matrix, where n is the number of
%   'observations' to be classified, and p is the dimension of each of
%   these observations
%   - alpha : CRP concentration parameter
%   - options : a structure containing infos regarding initialization and
%   inversion scheme (can be left empty -> defaults):
%       .priors : structure of priors for generative density
%           .mu0: px1 prior expectation of Gaussian component mean
%           .S0: prior covariance matrix of Gaussian component mean
%           .a0: prior counts for each class
%           .b0: prior sum of squared error within each class
%           NB: a0./b0 is the a priori expected precision (inverse
%           variance) of Gaussian components.
%       .TolFun = min increment of the free energy {1e-3}
%       .MaxIter = maximum number of iterations {128}
%       .MinIter = minimum number of iterations {1}
%       .verbose = flag for verbose mode {0}
%
% OUT:
%   - post : posterior structure:
%       .xi: Kxn matrix of labels probabilities (K=estimated number of
%       classes)
%       .mu: pxK matrix of component estimated mean
%       .S: Kx1 cell array of posterior covariance matrices over component
%       means
%       .a: Kx1 vector of estimated counts for each class
%       .b: Kx1 vector of etimated sum of squared error within each class
%       NB: a./b gives the expected precision (inverse variance) of each
%       Gaussian component!
%   - out : output structure containing:
%       .xih: the series of estimated labels
%       .F: lower bound on the log model evidence

disp('-- Variational Bayesian CRP classification --')

[p,n] = size(y); % dimensions of the observations

% options structure
if ~exist('alpha','var') || isempty(alpha)
    alpha = 1;
end