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Monte Carlo Markov Chain for inferring parameters for an Ordinary Differential Equation model

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This function uses a Monte Carlo Markov Chain algorithm to infer parameters for an ODE model

test_gamma()
function test_gamma()

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Name - test_gamma
% Creation Date - 17th Feb 2012
% Author - Soumya Banerjee
% Website - www.cs.unm.edu/~soumya
%
%
% Description - 
%   Function to experiemnt with different parameters for the gamma
%   distribution and find out the best values to get capsigma_powerminus2
%   equal to roughly 0.3 with tight distribution around it
%
%
% License - BSD 
%
% Change History - 
%                   17th Feb 2012 - Creation by Soumya Banerjee
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% a = 0; b = 0.01;
% a = 3; b = 0.01;
% for i = 1:1000
%     x(i) = gamrnd(a + 36,b);
% end

a = 23; b = 0.01;
m1 = 5; m2 = 5; m3 = 5;
for i = 1:1000
    x(i) = gamrnd(a + m1 + m2 + m3,b);
end

% figure
% plot(x)
figure
hist(x,20)

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