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In this video, I share a perspective on probability distributions that makes understanding and retaining them easier.

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Sources and Learning More

The incredible chart mapping out the relationships among probability distributions comes from Larry Leemis and Jacquelyn McQueston[1]. They and many other contributors put together a website providing all the details on that chart : [ Ссылка ]

Wikipedia has two [2][3] articles that provided a lot of interesting relationships. Also, John D. Cook proves that the student-T is a mixture of normals in [4].

[1] Leemis, L. & McQueston, J.T. (2008), Univariate Distribution Relationships, The American Statistician, Vol. 62, No. 1

[2] Relationships among probability Distribution, Wikipedia, [ Ссылка ]

[3] Compound Probability Distribution, Wikipedia, [ Ссылка ]

[4] Cook, J.D. (2008), Student-T as a Mixture of Normals, [ Ссылка ]

Extra Notes

In this video, I label the Poisson a "continuous" distribution, which is certainly not true in the general context - it is as discrete as they come. A better label than "continuous" would directly reference the specific limit that is taking place. Also, such a label would separate them from the other continuous distributions that can be reached with different limits. Thank you mCoding for pointing this out.

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