Joint Probability Distributions
The joint probability distribution quantifies the joint dependence between two random variables, X and Y. If these random variables are independent, then P(X=x,Y=y) = P(X=x)P(Y=y). This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company %%% CHAPTERS %%% 00:00 Intro 01:22 Examples & Motivation 09:49 Independence in Joint Distributions 14:02 Outro
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Joint Probability Distributions: Marginal and Conditional Densities
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Covariance and Correlation in Probability
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Random Variables and Probability Distributions
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The Expected Value (Mean) of a Probability Distribution
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Bayes theorem, the geometry of changing beliefs
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Bernoulli and Binomial Random Variables
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Probability - Joint Probability & Double Integral
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02 - Random Variables and Discrete Probability Distributions
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Joint probability density function problems for continuous r.v.[Marginal, conditional probability]
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Continuous Probability Distributions - Basic Introduction
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Probability Distribution Functions (PMF, PDF, CDF)
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The Strange Math That Predicts (Almost) Anything
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