| S# |
Lecture |
Course |
Institute |
Instructor |
Discipline |
| 4201 |
L02.3 A Die Roll Example (M-I-T)
|
Lecture 2: Conditioning and Bayes' Rule (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4202 |
L02.4 Conditional Probabilities Obey the Same Axioms (M-I-T)
|
Lecture 2: Conditioning and Bayes' Rule (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4203 |
L02.5 A Radar Example and Three Basic Tools (M-I-T)
|
Lecture 2: Conditioning and Bayes' Rule (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4204 |
L02.6 The Multiplication Rule (M-I-T)
|
Lecture 2: Conditioning and Bayes' Rule (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4205 |
L03.10 The King's Sibling (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4206 |
L02.7 Total Probability Theorem (M-I-T)
|
Lecture 2: Conditioning and Bayes' Rule (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4207 |
L03.1 Lecture Overview (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4208 |
L02.8 Bayes' Rule (M-I-T)
|
Lecture 2: Conditioning and Bayes' Rule (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4209 |
L03.2 A Coin Tossing Example (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4210 |
L03.3 Independence of Two Events (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4211 |
L03.4 Independence of Event Complements (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4212 |
L03.5 Conditional Independence (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4213 |
L03.6 Independence Versus Conditional Independence (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4214 |
L03.7 Independence of a Collection of Events (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4215 |
L03.8 Independence Versus Pairwise Independence (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4216 |
L04.1 Lecture Overview (M-I-T)
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4217 |
L03.9 Reliability (M-I-T)
|
Lecture 3: Independence (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4218 |
L04.2 The Counting Principle (M-I-T)
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4219 |
L04.3 Die Roll Example (M-I-T)
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4220 |
L04.4 Combinations (M-I-T)
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4221 |
L04.5 Binomial Probabilities (M-I-T)
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4222 |
L04.6 A Coin Tossing Example (M-I-T)
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4223 |
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4224 |
L04.8 Each Person Gets An Ace (M-I-T)
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4225 |
L04.9 Multinomial Probabilities (M-I-T)
|
Lecture 4: Counting (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
