%function rho = MAP_norm(unnorm_controls,alpha,t)
%	takes as input the unnormalized control data, and regularization parameters alpha and t
%	returns the normalization constants (as log values)

function rho = MAP_norm(unnorm_controls,alpha,t)

[M,N] = size(unnorm_controls);	%#rows = #controls, #col = #expt

%set the initial parameters:
newrho = zeros(1,N);			%matrix containing zeros, our initial estimate of norm constants
rho = ones(1,N);				%matrix containing ones, to ensure our loop runs the first time

while max(abs(rho-newrho))>0.000001		%calculates rho to a precision of 0.000001
   rho = newrho;									%sets the old value = new value

   
   %!YOUR CODE HERE - you need to duplicate your code for ML_norm,
   %!just changing the equation for calculation of sigma2
   
end
rho = newrho;		%best estimate of normalization constants



return