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| − | In Lecture 16, | + | In Lecture 16, we saw an example where the concept of expectation of a random variable can be used to decide on the best winning strategy. For 1D discrete random variables, we also learned the definition of "moment of order k", and that of "centralized moment of order k". We then focused on the centralized moment of order two, a quantity called the "variance" of the random variable. We noted that adding a constant to the random variable does not change the variance, and that multiplying the random variable by a constant "a" has the effect of multiplying the variance by <math>a^2</math>. |
Revision as of 12:34, 13 February 2013
Lecture 16 Blog, ECE302 Spring 2013, Prof. Boutin
Wednesday February 13, 2013 (Week 6) - 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)
In Lecture 16, we saw an example where the concept of expectation of a random variable can be used to decide on the best winning strategy. For 1D discrete random variables, we also learned the definition of "moment of order k", and that of "centralized moment of order k". We then focused on the centralized moment of order two, a quantity called the "variance" of the random variable. We noted that adding a constant to the random variable does not change the variance, and that multiplying the random variable by a constant "a" has the effect of multiplying the variance by $ a^2 $.
Action items for students (to be completed before next lecture)
- Read Sections 3.4 and 3.5 in the textbook.
- Solve the following problems from this problem set by Illowsky and Dean
- 3,7,9,11,16,27
Previous: Lecture 15
Next: Lecture 17
