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In Lecture 39, we continued trying to understand the relationship between the Poisson random process and the binomial counting process. First we re-emphasized how the pmf of a Poisson random variable approximates the pmf of a binomial random variable by looking at a chess example (number of matches lost by Kasparov playing against n players.) We then went over the "true" definition of a Poisson process, where the process is described as a counting process with 3 properties (time homogeneity, independence, and small interval probability). We spent a lot of time explaining the third property. | In Lecture 39, we continued trying to understand the relationship between the Poisson random process and the binomial counting process. First we re-emphasized how the pmf of a Poisson random variable approximates the pmf of a binomial random variable by looking at a chess example (number of matches lost by Kasparov playing against n players.) We then went over the "true" definition of a Poisson process, where the process is described as a counting process with 3 properties (time homogeneity, independence, and small interval probability). We spent a lot of time explaining the third property. | ||
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==Action items for students (to be completed before next lecture)== | ==Action items for students (to be completed before next lecture)== | ||
*Read Section 9.4 in the textbook. | *Read Section 9.4 in the textbook. | ||
*Solve problems 9.34, 9.35, 9.36 in the textbook. (You will hand in your solution as part of homework 7.) | *Solve problems 9.34, 9.35, 9.36 in the textbook. (You will hand in your solution as part of homework 7.) | ||
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Previous: [[Lecture38_blog_ECE302S13_Boutin|Lecture 38]] | Previous: [[Lecture38_blog_ECE302S13_Boutin|Lecture 38]] | ||
Latest revision as of 09:09, 17 April 2013
Lecture 39 Blog, ECE302 Spring 2013, Prof. Boutin
Monday April 15, 2013 (Week 15) - 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, 31, 32, 33, 34, 35, 36, 37, 38
In Lecture 39, we continued trying to understand the relationship between the Poisson random process and the binomial counting process. First we re-emphasized how the pmf of a Poisson random variable approximates the pmf of a binomial random variable by looking at a chess example (number of matches lost by Kasparov playing against n players.) We then went over the "true" definition of a Poisson process, where the process is described as a counting process with 3 properties (time homogeneity, independence, and small interval probability). We spent a lot of time explaining the third property.
Action items for students (to be completed before next lecture)
- Read Section 9.4 in the textbook.
- Solve problems 9.34, 9.35, 9.36 in the textbook. (You will hand in your solution as part of homework 7.)
Previous: Lecture 38
Next: Lecture 40
