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[[Lecture30_blog_ECE302S13_Boutin|30]]) | [[Lecture30_blog_ECE302S13_Boutin|30]]) | ||
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| − | We began Lecture 11 with a last example of a 2D random variable. We then presented three important discrete variables: the binomial random variable, the geometric random variable, and the Poisson random variable. We then moved on to the topic of "functions of a random variable". The formula for obtaining the probability mass function of a function of a random variable was given, and we illustrated it with two simple examples. We finished the lecture with a short illustration of a research-level image processing problem for which discrete distributions are useful. | + | We began Lecture 11 with a last example of a 2D random variable. We then presented three important discrete variables: the binomial random variable, the geometric random variable, and the Poisson random variable. We then moved on to the topic of "functions of a random variable". The formula for obtaining the probability mass function of a function of a random variable was given, and we illustrated it with two simple examples. We finished the lecture with a short illustration of a research-level image processing problem for which discrete distributions are useful. |
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==Action items for students (to be completed before next lecture)== | ==Action items for students (to be completed before next lecture)== | ||
| + | *None. Just keep preparing for the test. | ||
Previous: [[Lecture10_blog_ECE302S13_Boutin|Lecture 10]] | Previous: [[Lecture10_blog_ECE302S13_Boutin|Lecture 10]] | ||
Latest revision as of 09:26, 4 February 2013
Lecture 11 Blog, ECE302 Spring 2013, Prof. Boutin
Friday February 1, 2013 (Week 4) - See Course Outline.
(Other blogs 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
We began Lecture 11 with a last example of a 2D random variable. We then presented three important discrete variables: the binomial random variable, the geometric random variable, and the Poisson random variable. We then moved on to the topic of "functions of a random variable". The formula for obtaining the probability mass function of a function of a random variable was given, and we illustrated it with two simple examples. We finished the lecture with a short illustration of a research-level image processing problem for which discrete distributions are useful.
Action items for students (to be completed before next lecture)
- None. Just keep preparing for the test.
Previous: Lecture 10
Next: Lecture 12
