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_GPclass.m
    path of file: ./repos/spm12/toolbox/Shoot
    
    the code of the file until where you have to start completion: function [varargout] = spm_GPclass(varargin)
% Gaussian process classification
% [p,F,K,theta,bic] = spm_GPclass(XX,t,lab,cov_fun,fun_args)
% Inputs:
%     XX       - cell array of dot product matrices
%                for training and testing data
%     t        - target values for training data
%     lab      - binary array indicating which are training data
%     cov_fun  - function for building covariance matrix
%     fun_args - additional arguments for covariance function
% Outputs
%     p     - Belonging probabilities
%     F     - Log-likelihood
%     K     - Covariance matrix
%     bic   - Adjustment to log-likelihood to account for hyper-parameter estimation
%
% See Chapter 3 o
    
function bic = LaplaceAdjustment(t,X,cov_fun,theta)
% Compute adjustment to marginal log-likelihood to account for uncertainty
% of estimating theta
H     = Hessian(t,X,cov_fun,theta);
[V,D] = eig(H);
d     = diag(D);
lam   = 1e-6;  % Cross-check against later definition
d     = max(d,lam);
bic   = 0.5*(numel(d)*log(lam) - sum(log(d)));
%__________________________________________________________________________
%__________________________________________________________________________
function [H,E0] = Hessian(t,X,cov_fun,theta)
dtheta = 0.05;
H      = zeros(numel(theta));
E0     = objfun(theta,t,X,cov_fun);
for i=1:numel(theta)
    thetap    = theta;
    thetap(i) = thetap(i)+dtheta;
    thetan    = theta;
    thetan(i) = thetan(i)-dtheta;
    Ep        = objfun(thetap,t,X,cov_fun);
    En        = objfun(thetan,t,X,cov_fun);
    H(i,i)    = (Ep+En - 2*E0)/dtheta^2;
    for j=(i+1):numel(theta)
        thetapp    = thetap;
        thetapp(j) = thetapp(j)+dtheta;
        Epp        = objfun(thetapp,t,X,cov_fun);
        thetapn    = thetap;
        thetapn(j) = thetapn(j)-dtheta;
        Epn        = objfun(thetapn,t,X,cov_fun);
        gp         = (Epp-Epn)/2/dtheta;

        thetanp    = thetan;
        thetanp(j) = thetanp(j)+dtheta;
        Enp        = objfun(thetanp,t,X,cov_fun);
        thetann    = thetan;
        thetann(j) = thetann(j)-dtheta;
        Enn        = objfun(thetann,t,X,cov_fun);
        gn         = (Enp-Enn)/2/dtheta;
        H(i,j)     = (gp-gn)/2/dtheta;
        H(j,i)     = H(i,j);
    end