Generating function expectation

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SUMMARY

The discussion focuses on the concept of generating functions in probability theory, specifically how to derive the expectation of a joint distribution function, f(x,y), from its bivariate generating function, G(m,n). The expectation can be calculated using the derivative of the generating function, similar to univariate cases where the expectation of f(x) is obtained from G'(1). The conversation emphasizes the importance of clearly defining the expected value in the context of joint distributions.

PREREQUISITES
  • Understanding of generating functions in probability theory
  • Familiarity with the concept of expected value
  • Knowledge of bivariate distributions
  • Basic calculus, specifically differentiation
NEXT STEPS
  • Study the derivation of expected values from generating functions
  • Explore joint probability distributions and their properties
  • Learn about the application of bivariate generating functions in statistics
  • Investigate advanced topics in probability theory, such as moment-generating functions
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Mathematicians, statisticians, and data scientists interested in probability theory, particularly those working with generating functions and joint distributions.

jimmy1
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A probability distribution,[tex]f(x)[/tex] ,can be represented as a generating function,[tex]G(n)[/tex], as [tex]\sum_{x} f(x) n^x[/tex]. The expectation of [tex]f(x)[/tex] can be got from [tex]G'(1)[/tex].

A bivariate generating function, [tex]G(m,n)[/tex] of the joint distribution [tex]f(x,y)[/tex] can be represented as [tex]\sum_{x} \sum_{y} f(x,y) n^x m^y[/tex].

Now my question is how can I get the expectation of [tex]f(x,y)[/tex] from the above generating function?
 
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Er, are you sure you're asking the right question? What meaning did you have in mind for "the expectation of f(x, y)"? Do you mean to think of f as the probability distribution for an R²-valued random variable, or something like that? Anyways, I would start by writing down the definition of expected value, and work from there.
 

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