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Home » Applied Sciences » Engineering » Electrical Engineering and Computer Science (M-I-T) » Introduction to Probability (Spring 2018) (M-I-T) » Part II: Inference & Limit Theorems (M-I-T) » Lecture 17: Linear Least Mean Squares (LLMS) Estimation (M-I-T) » L17.1 Lecture Overview (M-I-T)

L17.1 Lecture Overview (M-I-T)

Course:Lecture 17: Linear Least Mean Squares (LLMS) Estimation (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. John Tsitsiklis, Prof. Patrick Jaillet
Level:Graduate

Lecture 17: Linear Least Mean Squares (LLMS) Estimation (M-I-T)

L17.1 Lecture Overview (M-I-T)
L17.2 LLMS Formulation (M-I-T)
L17.3 Solution to the LLMS Problem (M-I-T)
L17.4 Remarks on the LLMS Solution and on the Error Variance (M-I-T)
L17.5 LLMS Example (M-I-T)
L17.6 LLMS for Inferring the Parameter of a Coin (M-I-T)
L17.7 LLMS with Multiple Observations (M-I-T)
L17.8 The Simplest LLMS Example with Multiple Observations (M-I-T)
L17.9 The Representation of the Data Matters in LLMS (M-I-T)

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