% BAYES regression - using linear equation
%% first we generate the data
w = rand(2,1);
x = [-1:0.01:1];
s_dev = 0.1;
y = [x' ones(201,1)]*w + randn(201,1)*s_dev;
plot(x,y,'rx');
% We assume next that the values in vector W are unknown BUT we keep it
% for REFERENCE
%% our problem is to find w_inf - the values of the inferred parameters
% in this setting w_inf is a RANDOM VARIABLE with Gaussian distribution
% with mean m_w and variance s_w
% The problem is to find the parameters of the distribution: m_m and s_v
% We have to write BAYES' formula
% Have to compute the POSTERIOR distribution
% The POSTERIOR DISTRIBUTION of w_inf is a two-dimensional GAUSSIAN
% We generate 20 examples
% We draw the equations
for ii = 1:20;
w_inf = wVect(:,ii);
y = [x' ones(201,1)]*w_inf;
plot(x,y);
end;