Probabilistic Systems Analysis and Applied Probability (Fall 2010) (M-I-T)

• Lecture 1: Probability Models and Axioms (M-I-T)
• Lecture 2: Conditioning and Bayes' Rule (M-I-T)
• Lecture 3: Independence (M-I-T)
• Lecture 4: Counting (M-I-T)
• Lecture 5: Discrete Random Variables; Probability Mass Functions; Expectations (M-I-T)
• Lecture 6: Discrete Random Variable Examples; Joint PMFs (M-I-T)
• Lecture 7: Multiple Discrete Random Variables: Expectations, Conditioning, Independence (M-I-T)
• Lecture 8: Continuous Random Variables (M-I-T)
• Lecture 10: Continuous Bayes' Rule; Derived Distributions (M-I-T)
• Lecture 11: Derived Distributions; Convolution; Covariance and Correlation (M-I-T)
• Lecture 12: Iterated Expectations; Sum of a Random Number of Random Variables (M-I-T)
• Lecture 13: Bernoulli Process (M-I-T)
• Lecture 14: Poisson Process I (M-I-T)
• Lecture 15: Poisson Process II (M-I-T)
• Lecture 16: Markov Chains I (M-I-T)
• Lecture 17: Markov Chains II (M-I-T)
• Lecture 18: Markov Chains III (M-I-T)
• Lecture 19: Weak Law of Large Numbers (M-I-T)
• Lecture 20: Central Limit Theorem (M-I-T)
• Lecture 21: Bayesian Statistical Inference I (M-I-T)
• Lecture 22: Bayesian Statistical Inference II (M-I-T)
• Lecture 23: Classical Statistical Inference I (M-I-T)
• Lecture 24: Classical Inference II (M-I-T)
• Lecture 25: Classical Inference III; Course Overview (M-I-T)