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E1.5. Joint, marginal conditional distributions, and Bayes' Rule (Econometrics Math)
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In this video, I define joint, marginal and conditional distributions (pmfs or pdfs). I motivate these definitions using a simple example of two discrete random variables and their joint distribution.
After motivating all three types of distributions, I discuss two aspects of these concepts that are important for econometrics: independence and Bayes' Rule.
In the discussion of independence, I offer a useful test for detecting non-independence of random variables.
In the discussion of Bayes' Rule, I actually derive the formula from formulas for conditional probabilities and marginal probabilities. My derivation relies heavily on the intuition that I use to introduce these concepts earlier in the video.
After motivating all three types of distributions, I discuss two aspects of these concepts that are important for econometrics: independence and Bayes' Rule.
In the discussion of independence, I offer a useful test for detecting non-independence of random variables.
In the discussion of Bayes' Rule, I actually derive the formula from formulas for conditional probabilities and marginal probabilities. My derivation relies heavily on the intuition that I use to introduce these concepts earlier in the video.
E1.5. Joint, marginal conditional distributions, and Bayes' Rule (Econometrics Math)
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