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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 I: The Fundamentals (M-I-T) » Lecture 2: Conditioning and Bayes' Rule (M-I-T)

Lecture 2: Conditioning and Bayes' Rule (M-I-T)

S# Lecture Course Institute Instructor Discipline
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L02.1 Lecture Overview (M-I-T)
Lecture 2: Conditioning and Bayes' Rule (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
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L02.2 Conditional Probabilities (M-I-T)
Lecture 2: Conditioning and Bayes' Rule (M-I-T) MIT Prof. John Tsitsiklis, Prof. Patrick Jaillet Applied Sciences
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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
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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
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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
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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
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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
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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