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(8 Lectures Available)

S#	Lecture	Course	Institute	Instructor	Discipline
1	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
2	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
3	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
4	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
5	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
6	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
7	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
8	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