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Lecture 1: Probability Models and Axioms
Lecture 2: Conditioning and Bayes' Rule
Lecture 3: Independence
Lecture 4: Counting
Lecture 5: Discrete Random Variables; Probability Mass Functions; Expectations
Lecture 6: Discrete Random Variable Examples; Joint PMFs
Lecture 7: Multiple Discrete Random Variables: Expectations, Conditioning, Independence
Lecture 8: Continuous Random Variables
Lecture 9: Multiple Continuous Random Variables
Lecture 10: Continuous Bayes' Rule; Derived Distributions
Lecture 11: Derived Distributions; Convolution; Covariance and Correlation
Lecture 12: Iterated Expectations; Sum of a Random Number of Random Variables
Lecture 13: Bernoulli Process
Lecture 14: Poisson Process I
Lecture 15: Poisson Process II
Lecture 16: Markov Chains I
Lecture 17: Markov Chains II
Lecture 18: Markov Chains III
Lecture 19: Weak Law of Large Numbers
Lecture 20: Central Limit Theorem
Lecture 21: Bayesian Statistical Inference I
Lecture 22: Bayesian Statistical Inference II
Lecture 23: Classical Statistical Inference I
Lecture 24: Classical Inference II
Lecture 25: Classical Inference III; Course Overview