| Line 21: | Line 21: | ||
hold on; | hold on; | ||
plot(num_samples, sigmahat); | plot(num_samples, sigmahat); | ||
| + | |||
| + | |||
| + | --[[User:Han84|Han84]] 22:49, 2 April 2010 (UTC) | ||
Need real database? Look it up in this website: | Need real database? Look it up in this website: | ||
| + | |||
http://archive.ics.uci.edu/ml/datasets.html | http://archive.ics.uci.edu/ml/datasets.html | ||
| + | |||
have fun! | have fun! | ||
| − | |||
| − | + | Golsa | |
| − | + | ||
Revision as of 08:34, 4 April 2010
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.
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);
--Han84 22:49, 2 April 2010 (UTC)
Need real database? Look it up in this website:
http://archive.ics.uci.edu/ml/datasets.html
have fun!
Golsa
