(27 intermediate revisions by one other user not shown)
Line 1: Line 1:
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.
+
[[Category:2010 Spring ECE 662 mboutin]]
  
Below are the notes from lecture.
+
=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.  
  
== Maximum Likelihood Estimation (MLE) ==
+
Note for this lecture can be found [[noteslecture11ECE662S10|here]].
 +
  
----
+
Previous: [[Lecture10ECE662S10|Lecture 10]]
 +
Next: [[Lecture12ECE662S10|Lecture 12]]
  
General Principles:
+
----
Given vague knowledge about a situation and some training data (i.e. feature vector values for which the class is known)
+
[[ 2010 Spring ECE 662 mboutin|Back to 2010 Spring ECE 662 mboutin]]
<math>x_l, l=1,\ldots,\text{hopefully large number}</math>
+

Latest revision as of 09:15, 11 May 2010


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.


Previous: Lecture 10 Next: Lecture 12


Back to 2010 Spring ECE 662 mboutin

Alumni Liaison

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

Francisco Blanco-Silva