| Line 5: | Line 5: | ||
The code for this graph is like below. | The code for this graph is like below. | ||
| + | <nowiki> | ||
samples_step = 3; | samples_step = 3; | ||
num_samples = samples_step:samples_step:10000; | num_samples = samples_step:samples_step:10000; | ||
| Line 21: | Line 22: | ||
hold on; | hold on; | ||
plot(num_samples, sigmahat); | plot(num_samples, sigmahat); | ||
| + | </nowiki> | ||
--[[User:Han84|Han84]] 22:49, 2 April 2010 (UTC) | --[[User:Han84|Han84]] 22:49, 2 April 2010 (UTC) | ||
Revision as of 18:04, 2 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)
