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: demo_staticModel.m
    path of file: ./repos/VBA-toolbox/demos/0_basics
    
    the code of the file until where you have to start completion: function [posterior, out] = demo_staticModel ()
% // VBA toolbox //////////////////////////////////////////////////////////
%
% [posterior, out] = demo_staticModel ()
% Demo of simple linear Bayesian regression
%
% The problem is to fit observations with a linear combination
% of regressors. Formally, this can be written as:
%
% $$ y = X.\beta + \vareps $$
%
% where $y$ is the set of observations, $X$ is the design matrix containing
% the regressors, $\beta$ is a vector of parameters defining the respective 
% weight of the regressors, and $\vareps ~ N(0,\sigma^2)$ is some measurment
% noise.
% The objective is thus to estimate $\beta$ and $\sigma^2$ from the data
% given X.
%
% /////////////////////////////////////////////////////////////////////////

%% Specify the linear model
% =========================================================================

% number of observations
N = 100;

% regressors
k = 1; % this is a simple regression. Set k > 1 for a multiple regression model

    
function [posterior, out] = demo_staticModel ()
% // VBA toolbox //////////////////////////////////////////////////////////
%
% [posterior, out] = demo_staticModel ()
% Demo of simple linear Bayesian regression
%
% The problem is to fit observations with a linear combination
% of regressors. Formally, this can be written as:
%
% $$ y = X.\beta + \vareps $$
%
% where $y$ is the set of observations, $X$ is the design matrix containing
% the regressors, $\beta$ is a vector of parameters defining the respective 
% weight of the regressors, and $\vareps ~ N(0,\sigma^2)$ is some measurment
% noise.
% The objective is thus to estimate $\beta$ and $\sigma^2$ from the data
% given X.
%
% /////////////////////////////////////////////////////////////////////////

%% Specify the linear model
% =========================================================================

% number of observations
N = 100;

% regressors
k = 1; % this is a simple regression. Set k > 1 for a multiple regression model
regressors = randn(k, N);

% observation function that links inputs (regressors), parameters (beta
% weights) and observations
function [gx] = g_linearRegression (~, betas, regressors, ~)
    % predictied observations
    gx = betas' * regressors ;
end