| S# |
Lecture |
Course |
Institute |
Instructor |
Discipline |
| 4426 |
|
Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4427 |
L17.8 The Simplest LLMS Example with Multiple Observations (M-I-T)
|
Lecture 17: Linear Least Mean Squares (LLMS) Estimation (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4428 |
L18.6 Convergence in Probability (M-I-T)
|
Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4429 |
L19.1 Lecture Overview (M-I-T)
|
Lecture 19: The Central Limit Theorem (CLT) (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4430 |
L18.7 Convergence in Probability Examples (M-I-T)
|
Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4431 |
L17.9 The Representation of the Data Matters in LLMS (M-I-T)
|
Lecture 17: Linear Least Mean Squares (LLMS) Estimation (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4432 |
L19.2 The Central Limit Theorem (M-I-T)
|
Lecture 19: The Central Limit Theorem (CLT) (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4433 |
L18.8 Related Topics (M-I-T)
|
Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4434 |
L19.3 Discussion of the CLT (M-I-T)
|
Lecture 19: The Central Limit Theorem (CLT) (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4435 |
S18.1 Convergence in Probability of the Sum of Two Random Variables (M-I-T)
|
Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4436 |
L19.4 Illustration of the CLT (M-I-T)
|
Lecture 19: The Central Limit Theorem (CLT) (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4437 |
S18.2 Jensen's Inequality (M-I-T)
|
Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4438 |
S18.3 Hoeffding's Inequality (M-I-T)
|
Lecture 18: Inequalities, Convergence, and the Weak Law of Large Numbers (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4439 |
L19.5 CLT Examples (M-I-T)
|
Lecture 19: The Central Limit Theorem (CLT) (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4440 |
L20.10 Maximum Likelihood Estimation Examples (M-I-T)
|
Lecture 20: An Introduction to Classical Statistics (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4441 |
L19.6 Normal Approximation to the Binomial (M-I-T)
|
Lecture 19: The Central Limit Theorem (CLT) (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4442 |
L20.1 Lecture Overview (M-I-T)
|
Lecture 20: An Introduction to Classical Statistics (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4443 |
L19.7 Polling Revisited (M-I-T)
|
Lecture 19: The Central Limit Theorem (CLT) (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4444 |
L20.2 Overview of the Classical Statistical Framework (M-I-T)
|
Lecture 20: An Introduction to Classical Statistics (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4445 |
|
Introductory Quantum Mechanics II (M-I-T)
|
MIT
|
Prof. Dr. Robert Field and Prof. Dr. Andrei Tokmakoff
|
Basic and Health Sciences
|
| 4446 |
L20.3 The Sample Mean and Some Terminology (M-I-T)
|
Lecture 20: An Introduction to Classical Statistics (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4447 |
|
Introductory Quantum Mechanics II (M-I-T)
|
MIT
|
Prof. Dr. Robert Field and Prof. Dr. Andrei Tokmakoff
|
Basic and Health Sciences
|
| 4448 |
L20.4 On the Mean Squared Error of an Estimator (M-I-T)
|
Lecture 20: An Introduction to Classical Statistics (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4449 |
L21.10 The Poisson Approximation to the Binomial (M-I-T)
|
Lecture 21: The Bernoulli Process (M-I-T)
|
MIT
|
Prof. John Tsitsiklis, Prof. Patrick Jaillet
|
Applied Sciences
|
| 4450 |
|
Introductory Quantum Mechanics II (M-I-T)
|
MIT
|
Prof. Dr. Robert Field and Prof. Dr. Andrei Tokmakoff
|
Basic and Health Sciences
|
