% 2-param likelihood function.      Barnett 2/3/06

% generate some fake data... (you could replace this with real-world data)
n = 20;                        % amount of data to make
y = -log(rand(n,1));           % trick to get random samples from exp pdf
plot(y, zeros(size(y)), '*');  % examine your 'data' y. (zero vertical coord)

% calc likelihood using gamma pdf model...    2 params.    A bit slow!
r = 0.1:0.03:3;                  % param r
l = 0:0.01:2;                    % param l = lambda
[rr,ll] = meshgrid(r,l);         % create rr and ll as their values
                                 %    over a rectangular 2d grid.
L = ones(size(rr));              % L will be over same grid: start value 1
for i=1:n                        % loop i=1...n taking product of gamma pdfs
  L = L .* (ll.^rr ./ gamma(rr)) .* exp(-ll.*y(i)) .* y(i).^(rr-1);
end

% labeled plots...
figure; surf(r, l, L); xlabel('r'); ylabel('\lambda'); zlabel('L(r,\lambda)');
figure; contourf(r, l, L); xlabel('r'); ylabel('\lambda');
% done


% -------------------------------------------------------------------------
% Note there's a much faster way to compute the L values over the grid
% by taking advantage of the fact the y-dep part of L is a product of
% r-dep and l-dep factors. You don't even need the meshgrid cmd. Do this:

tic;                                                         % start the clock
lnL = n*kron(log(l)', r) - repmat(l'*sum(y), size(r)) + ...
      repmat(-n*log(gamma(r))+(r-1)*sum(log(y)), size(l'));
L = exp(lnL);
toc                                                          % read the clock
%   speed is 0.04 sec vs 2.4 sec for the crude above way!