Details of Lecture 11, ECE662 Spring 2010

In Lecture 11, we continued our discussion of Parametric Density Estimation techniques. We discussed the Maximum Likelihood Estimation (MLE) method and look at a couple of 1-dimension examples for case when feature in dataset follows Gaussian distribution. First, we looked at case where mean parameter was unknown, but variance parameter is known. Then we followed with another example where both mean and variance where unknown. Finally, we looked at the slight "bias" problem when calculating the variance.

Note for this lecture can be found here.


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