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: DEM_demo_LAP.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function DEM_demo_LAP
% This demonstration compares Generalised filtering under the Laplace
% assumption (spm_LAP) with variational filtering under the same fixed form
% approximation (i.e. DEM). We use a simple linear convolution model to
% illustrate the differences and similarities. The key difference between
% the two schemes lies (in this example) lies in estimates of conditional
% uncertainty. spm_LAP is must less over-confident because it eschews the
% means-field approximation implicit in DEM. The demonstration addresses 
% quadruple estimation of hidden states, exogenous input, parameters and 
% log-precisions (and, for spm_LAP, log-smoothness)
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging
 
% get basic convolution model
%==========================================================================
M       = spm_DEM_M('convolution model');

% rng('default');
 
% gradient functions for speed (not implemented here)
%--------------------------------------------------------------------------
% M(1).fx = inline('P.f','x','v','P');
% M(1).fv = inline('P.h','x','v','P');
% M(1).gx = inline('P.g','x','v','P');
% M(1).gv = inline('sparse(4,1)','x','v','P');
 
% free parameters
%--------------------------------------------------------------------------
P       = M(1).pE;                            % true parameters
ip      = [1 2 5 9];                          % free parameters
pE      = spm_vec(P);
np      = length(pE);
pE(ip)  = 0;
pE      = spm_unvec(pE,P);
pC      = sparse(ip,ip,exp(8),np,np);

    
function DEM_demo_LAP
% This demonstration compares Generalised filtering under the Laplace
% assumption (spm_LAP) with variational filtering under the same fixed form
% approximation (i.e. DEM). We use a simple linear convolution model to
% illustrate the differences and similarities. The key difference between
% the two schemes lies (in this example) lies in estimates of conditional
% uncertainty. spm_LAP is must less over-confident because it eschews the
% means-field approximation implicit in DEM. The demonstration addresses 
% quadruple estimation of hidden states, exogenous input, parameters and 
% log-precisions (and, for spm_LAP, log-smoothness)
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging
 
% get basic convolution model
%==========================================================================
M       = spm_DEM_M('convolution model');

% rng('default');
 
% gradient functions for speed (not implemented here)
%--------------------------------------------------------------------------
% M(1).fx = inline('P.f','x','v','P');
% M(1).fv = inline('P.h','x','v','P');
% M(1).gx = inline('P.g','x','v','P');
% M(1).gv = inline('sparse(4,1)','x','v','P');
 
% free parameters
%--------------------------------------------------------------------------
P       = M(1).pE;                            % true parameters
ip      = [1 2 5 9];                          % free parameters
pE      = spm_vec(P);
np      = length(pE);
pE(ip)  = 0;
pE      = spm_unvec(pE,P);
pC      = sparse(ip,ip,exp(8),np,np);
M(1).pE = pE;
M(1).pC = pC;
 
% free hyperparameters
%--------------------------------------------------------------------------
M(1).Q  = {speye(M(1).l,M(1).l)};
M(1).R  = {speye(M(1).n,M(1).n)};
M(1).hE = 6;
M(1).gE = 6;
M(1).hC = 1/4;
M(1).gC = 1/4;
 
% generate data and invert
%==========================================================================
M(1).E.nN = 16;                                % number of E steps
M(1).E.nE = 16;                                % number of E steps
M(1).E.nD = 1;                                 % number of time steps
M(1).E.s  = 1;                                 % smoothness
M(1).E.d  = 2;                                 % order
M(1).E.n  = 6;                                 % order
 
N         = 32;                                % length of data sequence
U         = exp(-((1:N) - 12).^2/(2.^2));      % this is the Gaussian cause
DEM       = spm_DEM_generate(M,U,{P},{8,16},{6});
 
 
% invert
%==========================================================================
spm_figure('GetWin','DEM');
LAP       = spm_LAP(DEM);
DEM       = spm_DEM(DEM);
 
% Show results for DEM
%==========================================================================
spm_figure('GetWin','Figure 1: DEM - mean-field assumption');
 
% overlay true values
%--------------------------------------------------------------------------
spm_DEM_qU(DEM.qU,DEM.pU)
 
% parameters
%--------------------------------------------------------------------------
qP    = spm_vec(DEM.qP.P);
qP    = qP(ip);
tP    = spm_vec(DEM.pP.P);
tP    = tP(ip);
 
subplot(2,2,4)
bar([tP qP])
axis square
legend