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: spm_rglm.m
    path of file: ./repos/malini/Codes/deconvolution - model/spm8/toolbox/mixture
    
    the code of the file until where you have to start completion: function [rglm,yclean] = spm_rglm (y,X,m,priors,verbose)
% Fit a Robust GLM 
% FORMAT [rglm,yclean] = spm_rglm (y,X,m,priors,verbose)
%
% The noise is modelled with a Mixture of Zero-Mean Gaussians 
%
% y          [N x 1] data vector
% X          [N x p] design matrix
% m          Number of mixture components
% priors     .alpha      [1 x 1] weight precision (default=0.001)

    
function [rglm,yclean] = spm_rglm (y,X,m,priors,verbose)
% Fit a Robust GLM 
% FORMAT [rglm,yclean] = spm_rglm (y,X,m,priors,verbose)
%
% The noise is modelled with a Mixture of Zero-Mean Gaussians 
%
% y          [N x 1] data vector
% X          [N x p] design matrix
% m          Number of mixture components
% priors     .alpha      [1 x 1] weight precision (default=0.001)
%            .mean_err   [m x 1] vector of mean error SD
%            .std_err    [m x 1] vector of dev of error SD
% verbose    0/1 to printout inner workings (default=0)
%
% rglm       Returned model 
% yclean     'Clean' data
%
% -------------------------------------------------------
% The fields in rglm are:
%
% m                The number of error components
% fm               The negative free energy
% loops            Number of iterations used
%
%                  In the field priors:
%
% lambda_0         Dirichlet parameters for mixing coeffs
% b_0,c_0          Gamma parameters for precisions
%
%                  In the field posts:
%
% lambda           Dirichlet parameters  for mixing coeffs
% b,c              Gamma parameters for precisions
% w_mean           Mean estimated regression coefficients
% w_cov            Covariance of regression coefficients
% pi               mixing coefficients (lambda/sum(lambda))
% variances        variances (1./(b.*c))
% gamma            the responsilities of each noise component
%___________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Will Penny 
% $Id: spm_rglm.m 1276 2008-03-28 18:29:19Z guillaume $

if nargin < 4 | isempty(priors)
    mean_alpha=0.001;
    % Set variance priors
    b_0=1000*ones(m,1);
    c_0=0.001*ones(m,1);
else
    mean_alpha=priors.alpha;
    for mm=1:m,
        b_0(mm)=(priors.std_err(mm)^2)/priors.mean_err(mm);
        c_0(mm)=priors.mean_err(mm)/b_0(mm);
    end