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: mess_g3.m
    path of file: ./repos/malini/Codes/FC_EC_calculation code/DynamicFC/jplv7/spatial/mess_models
    
    the code of the file until where you have to start completion: function results = mess_g3(y,x,options,ndraw,nomit,prior,start)
% PURPOSE: Bayesian estimates of the matrix exponential spatial model (mess)
% % [samples values of rho and #neighbors to produce a posterior distribution
%    for these hyperparameters in the problem]
% S*y = X*b + e,           with xflag == 0, or:
% S*y = [i X D*X]*b + e,   with xflag == 1
%          e = N(0,sige*In), 
%          S = e^alpha*D
%          B = N(c,T), 
%          1/sige = Gamma(nu,d0), 

    
function results = mess_g3(y,x,options,ndraw,nomit,prior,start)
% PURPOSE: Bayesian estimates of the matrix exponential spatial model (mess)
% % [samples values of rho and #neighbors to produce a posterior distribution
%    for these hyperparameters in the problem]
% S*y = X*b + e,           with xflag == 0, or:
% S*y = [i X D*X]*b + e,   with xflag == 1
%          e = N(0,sige*In), 
%          S = e^alpha*D
%          B = N(c,T), 
%          1/sige = Gamma(nu,d0), 
%          alpha = N(a,B), (default: diffuse prior) 
% D = a weight matrix constructed from neighbors using:
% D = sum rho^i N_i / sum rho^i, i=1,...,#neighbors
% using a grid of rho values from options.rmin to options.rmax
% and a grid of values options.nmin to options.nmax over the # of neighbors, i
% where i = # neighbors, N_i a matrix with ith nearest neighbors
%-------------------------------------------------------------
% USAGE: results = mess_g3(y,x,options,ndraw,nomit,prior,start)
% where: y = depend