%function rho = ML_norm(unnorm_controls)
%	takes as input the unnormalized control data
%	returns the normalization constants (as log values)

function rho = ML_norm(unnorm_controls)

[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 calculate the values of the parameters mu,
   %sigma2 and newrho.  Remember the Matlab function to replicate a matrix:
   %    repmat(matrix, num_rows, num_columns)
   %It is very helpful in completing this function.
  
   
end
rho = newrho;		%best estimate of normalization constants

return