Hello Ida,
I understand that you would like to pass a matrix of parameters into the mle function, and allow the same function call to occur no matter the size of the matrix. The issue is that mle will call the custom logpdf function with the parameters matrix, with each entry in the matrix as a separate input to the function.
I am not certain that it is the best way, but you can use varargin when defining the function handle to capture all of the parameter inputs in one cell array. You'll need a separate parameter for the size of the matrix, and then you can use cell2mat and reshape to turn the cell array back into the matrix you want.
As a side note, you can also pass the values of p and b as "constant" input arguments, rather than having to declare them as global variables.
function testMleWithMat
p = [0.5 1 0.7; 0.4 0.5 0.2]'; %some matrix
b = [2 .6 .3 .1]; %some other matrix
theta0 = [1 1; 1 1; 1 1; 1 1]'; %initial guess of unknowns
y_meas = [2.32 3.17 2.53]; %measured values
est = mle(y_meas,'logpdf',...
@(data,varargin) custlogpdf(data,p,b,size(theta0),varargin),...
'start',theta0);
function tot = custlogpdf(y_meas,p,b,dims,params)
theta = reshape(cell2mat(params),dims);
y_meas = y_meas'; % Gets permuted in mle
tot = sum(sum(y_meas.*log(b*(p*theta)')-b*(p*theta)'));
I hope that this helps with your optimization.
-Cam
