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Monday January 14, 2013 (Week 2) - See [[LectureScheduleECE302Spring13_Boutin|Course Outline]]. | Monday January 14, 2013 (Week 2) - See [[LectureScheduleECE302Spring13_Boutin|Course Outline]]. | ||
| − | + | (Other blogs [[Lecture1_blog_ECE302S13_Boutin|1]], | |
| + | [[Lecture2_blog_ECE302S13_Boutin|2]], | ||
| + | [[Lecture3_blog_ECE302S13_Boutin|3]], | ||
| + | [[Lecture4_blog_ECE302S13_Boutin|4]], | ||
| + | [[Lecture5_blog_ECE302S13_Boutin|5]], | ||
| + | [[Lecture6_blog_ECE302S13_Boutin|6]], | ||
| + | [[Lecture7_blog_ECE302S13_Boutin|7]], | ||
| + | [[Lecture8_blog_ECE302S13_Boutin|8]], | ||
| + | [[Lecture9_blog_ECE302S13_Boutin|9]], | ||
| + | [[Lecture10_blog_ECE302S13_Boutin|10]], | ||
| + | [[Lecture11_blog_ECE302S13_Boutin|11]], | ||
| + | [[Lecture12_blog_ECE302S13_Boutin|12]], | ||
| + | [[Lecture13_blog_ECE302S13_Boutin|13]], | ||
| + | [[Lecture14_blog_ECE302S13_Boutin|14]], | ||
| + | [[Lecture15_blog_ECE302S13_Boutin|15]], | ||
| + | [[Lecture16_blog_ECE302S13_Boutin|16]], | ||
| + | [[Lecture17_blog_ECE302S13_Boutin|17]], | ||
| + | [[Lecture18_blog_ECE302S13_Boutin|18]], | ||
| + | [[Lecture19_blog_ECE302S13_Boutin|19]], | ||
| + | [[Lecture20_blog_ECE302S13_Boutin|20]], | ||
| + | [[Lecture21_blog_ECE302S13_Boutin|21]], | ||
| + | [[Lecture22_blog_ECE302S13_Boutin|22]], | ||
| + | [[Lecture23_blog_ECE302S13_Boutin|23]], | ||
| + | [[Lecture24_blog_ECE302S13_Boutin|24]], | ||
| + | [[Lecture25_blog_ECE302S13_Boutin|25]], | ||
| + | [[Lecture26_blog_ECE302S13_Boutin|26]], | ||
| + | [[Lecture27_blog_ECE302S13_Boutin|27]], | ||
| + | [[Lecture28_blog_ECE302S13_Boutin|28]], | ||
| + | [[Lecture29_blog_ECE302S13_Boutin|29]], | ||
| + | [[Lecture30_blog_ECE302S13_Boutin|30]]) | ||
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In the fourth lecture, we defined the conditional probability P(A|B) and illustrated how to compute it with two examples. We also constructed a decision tree to illustrate how the the concept of conditional probabilities can be used to obtain the probability of false alarm and the probability of missed detection in a detection experiment. | In the fourth lecture, we defined the conditional probability P(A|B) and illustrated how to compute it with two examples. We also constructed a decision tree to illustrate how the the concept of conditional probabilities can be used to obtain the probability of false alarm and the probability of missed detection in a detection experiment. | ||
Latest revision as of 08:08, 25 January 2013
Lecture 4 Blog, ECE302 Spring 2013, Prof. Boutin
Monday January 14, 2013 (Week 2) - 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 the fourth lecture, we defined the conditional probability P(A|B) and illustrated how to compute it with two examples. We also constructed a decision tree to illustrate how the the concept of conditional probabilities can be used to obtain the probability of false alarm and the probability of missed detection in a detection experiment.
Relevant Links
- The game show that inspired the Monty Hall problem?
Action items for students (to be completed before next lecture)
- Read subsection 2.5 of the textbook.
- Solve the following problem in the textbook (do not hand in yet. These will be part of the second homework assignment.)
- 2.62
- Solve the following problem and share your solutions/comments/questions.
Previous: Lecture 3
Next: Lecture 5
