| S# |
Lecture |
Course |
Institute |
Instructor |
Discipline |
| 1 |
L10.10 Detection of a Binary Signal (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 2 |
L10.11 Inference of the Bias of a Coin (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 3 |
L10.1 Lecture Overview (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4 |
L10.2 Conditional PDFs (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 5 |
L10.3 Comments on Conditional PDFs (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 6 |
L10.4 Total Probability & Total Expectation Theorems (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 7 |
L10.5 Independence (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 8 |
L10.6 Stick-Breaking Example (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 9 |
L10.7 Independent Normals (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 10 |
L10.8 Bayes Rule Variations (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 11 |
L10.9 Mixed Bayes Rule (M-I-T)
|
Lecture 10: Continuous Random Variables Part III (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
