Revision as of 07:32, 4 April 2010 by Gmoayeri (Talk | contribs)

MATLAB has a "mle" function for maximum likelihood estimation. I think that this function is useful to verify the result of hw2 if you have MATLAB. I try to find the effect of the sample size in MLE using "mle" function because the number of samples is critical for estimation. To do this, I generate samples from normal distribution with mean as 0 and std as 5. The below graph shows the results of MLE according to the number of samples.

Mle samples.jpg

The code for this graph is like below.

   samples_step = 3;
   num_samples = samples_step:samples_step:10000;
   len = length(num_samples);
   mu = 0;
   sigma = 5;
   muhat = zeros(1, len);
   sigmahat = zeros(1, len);
   for x = num_samples
       data = mu + sigma * randn(1, x);
       phat = mle(data(1, :));
       muhat(1, x/samples_step) = phat(1);
       sigmahat(1, x/samples_step) = phat(2);
   end
   plot(num_samples, muhat);
   hold on;
   plot(num_samples, sigmahat);

Need real database? Look it up in this website: http://archive.ics.uci.edu/ml/datasets.html have fun! Golsa


--Han84 22:49, 2 April 2010 (UTC)

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Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva