Lecture 1: Probability and Counting | Statistics 110
46:29Lecture 2: Story Proofs, Axioms of Probability | Statistics 110
45:40Lecture 3: Birthday Problem, Properties of Probability | Statistics 110
48:54Lecture 4: Conditional Probability | Statistics 110
49:45Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110
50:01Lecture 6: Monty Hall, Simpson's Paradox | Statistics 110
49:00Lecture 7: Gambler's Ruin and Random Variables | Statistics 110
51:45Lecture 8: Random Variables and Their Distributions | Statistics 110
50:24Lecture 9: Expectation, Indicator Random Variables, Linearity (STAT 110)
50:23Lecture 10: Expectation Continued (STAT 110)
50:10Lecture 11: The Poisson Distribution (STAT 110)
42:46Lecture 12: Discrete vs. Continuous, the Uniform (STAT 110)
49:56Lecture 13: Normal Distribution (STAT 110)
51:10Lecture 14: Location, Scale, and LOTUS (STAT 110)
48:55Lecture 16: Exponential Distribution (STAT 110)
18:19Lecture 17: Moment Generating Functions (STAT 110)
50:45Lecture 18: MGFs Continued (STAT 110)
49:41Lecture 19: Joint, Conditional, and Marginal Distributions (STAT 110)
50:09Lecture 20: Multinomial and Cauchy (STAT 110)
49:00Lecture 21: Covariance and Correlation (STAT 110)
49:26Lecture 22: Transformations and Convolutions (STAT 110)
47:46Lecture 10: Expectation Continued (STAT 110)
This session rigorously proves linearity of expectation and applies it to solve a Putnam problem. It introduces the Negative Binomial distribution and analyzes the intriguing St. Petersburg Paradox.
We prove linearity of expectation, solve a Putnam problem, introduce the Negative Binomial distribution, and consider the St. Petersburg Paradox.
Lecture 9: Expectation, Indicator Random Variables, Linearity (STAT 110)
Next · Lecture 11Lecture 11: The Poisson Distribution (STAT 110)
Seminar notes & discussion
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