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Harvard STAT 110: Probability →0/21
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Lecture 1: Probability and Counting | Statistics 110

46:29
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Lecture 2: Story Proofs, Axioms of Probability | Statistics 110

45:40
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Lecture 3: Birthday Problem, Properties of Probability | Statistics 110

48:54
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Lecture 4: Conditional Probability | Statistics 110

49:45
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Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110

50:01
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Lecture 6: Monty Hall, Simpson's Paradox | Statistics 110

49:00
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Lecture 7: Gambler's Ruin and Random Variables | Statistics 110

51:45
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Lecture 8: Random Variables and Their Distributions | Statistics 110

50:24
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Lecture 9: Expectation, Indicator Random Variables, Linearity (STAT 110)

50:23
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Lecture 10: Expectation Continued (STAT 110)

50:10
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Lecture 11: The Poisson Distribution (STAT 110)

42:46
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Lecture 12: Discrete vs. Continuous, the Uniform (STAT 110)

49:56
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Lecture 13: Normal Distribution (STAT 110)

51:10
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Lecture 14: Location, Scale, and LOTUS (STAT 110)

48:55
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Lecture 16: Exponential Distribution (STAT 110)

18:19
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Lecture 17: Moment Generating Functions (STAT 110)

50:45
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Lecture 18: MGFs Continued (STAT 110)

49:41
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Lecture 19: Joint, Conditional, and Marginal Distributions (STAT 110)

50:09
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Lecture 20: Multinomial and Cauchy (STAT 110)

49:00
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Lecture 21: Covariance and Correlation (STAT 110)

49:26
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Lecture 22: Transformations and Convolutions (STAT 110)

47:46

Lecture 20: Multinomial and Cauchy (STAT 110)

Joe BlitzsteinHarvard University

Professor

49:00·2013·undergraduate level

This lecture introduces the Multinomial distribution, highlighting its properties and applications as a key multivariate discrete distribution. It also includes an example involving the Cauchy distribution.

MathematicsProbability & Information TheoryMultinomial DistributionCauchy DistributionProbability Properties

We introduce the Multinomial distribution, which is arguably the most important multivariate discrete distribution, and discuss its story and some of its nice properties, such as being able to "lump" categories together. We also do an example with the Cauchy distribution.

Note: Around , a factor of the square root of 2 was accidentally

dropped; the result should be 2 over the square root of pi, rather

Previous · Lecture 18

Lecture 19: Joint, Conditional, and Marginal Distributions (STAT 110)

Next · Lecture 20

Lecture 21: Covariance and Correlation (STAT 110)

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