| S# |
Lecture |
Course |
Institute |
Instructor |
Discipline |
| 4301 |
L11.3 A Linear Function of a Continuous Random Variable (M-I-T)
|
Lecture 11: Derived Distributions (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4302 |
Lecture 2.2: Flavor Symmetry (M-I-T)
|
Chapter 2. Symmetries (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4303 |
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
|
| 4304 |
L11.4 A Linear Function of a Normal Random Variable (M-I-T)
|
Lecture 11: Derived Distributions (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4305 |
Lecture 2.3: Parity (M-I-T)
|
Chapter 2. Symmetries (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4306 |
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
|
| 4307 |
L11.5 The PDF of a General Function (M-I-T)
|
Lecture 11: Derived Distributions (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4308 |
L11.6 The Monotonic Case (M-I-T)
|
Lecture 11: Derived Distributions (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4309 |
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
|
| 4310 |
Lecture 2.4: Charge Conjugation (M-I-T)
|
Chapter 2. Symmetries (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4311 |
L11.7 The Intuition for the Monotonic Case (M-I-T)
|
Lecture 11: Derived Distributions (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4312 |
|
Chapter 2. Symmetries (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4313 |
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
|
| 4314 |
L11.8 A Nonmonotonic Example (M-I-T)
|
Lecture 11: Derived Distributions (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4315 |
L11.9 The PDF of a Function of Multiple Random Variables (M-I-T)
|
Lecture 11: Derived Distributions (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4316 |
|
Lecture 11: Derived Distributions (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4317 |
Lecture 3.1: Introduction (M-I-T)
|
Chapter 3. Feynman Calculus (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4318 |
Lecture 3.2: Fermi's Golden Rule (M-I-T)
|
Chapter 3. Feynman Calculus (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4319 |
Lecture 3.3: Toy Theory (M-I-T)
|
Chapter 3. Feynman Calculus (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4320 |
Lecture 3.4: Higher-Order Diagrams (M-I-T)
|
Chapter 3. Feynman Calculus (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4321 |
Lecture 3.5: Divergency (M-I-T)
|
Chapter 3. Feynman Calculus (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4322 |
Lecture 4.10: Noether's Theorem (M-I-T)
|
Chapter 4. QED (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4323 |
Lecture 4.1: Free Wave Equation (M-I-T)
|
Chapter 4. QED (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4324 |
Lecture 4.2: Dirac Equation Solutions (M-I-T)
|
Chapter 4. QED (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
| 4325 |
Lecture 4.3: Antiparticles (M-I-T)
|
Chapter 4. QED (M-I-T)
|
MIT
|
Prof. Markus Klute
|
Basic and Health Sciences
|
