Transformation of random variable

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SUMMARY

The discussion centers on the transformation of a discrete random variable X into another variable Y through an arbitrary real-valued function g. The probability mass function of Y, denoted as f_Y(y), is expressed as f_Y(y) = |g^{-1}(y)|/n, where |g^{-1}(y)| represents the number of pre-images of y under g. The transformation relies on the uniform distribution of X, where each value x_i occurs with a probability of 1/n. Further simplification of this function requires additional information about the function g.

PREREQUISITES
  • Understanding of discrete random variables and their probability distributions
  • Familiarity with the concept of function transformations in probability theory
  • Knowledge of inverse functions and their implications in probability
  • Basic grasp of probability mass functions (PMFs)
NEXT STEPS
  • Study the properties of discrete random variables and their transformations
  • Learn about the implications of inverse functions in probability distributions
  • Explore examples of arbitrary functions g and their effects on probability mass functions
  • Investigate the conditions under which simplifications of probability functions are possible
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Students and professionals in statistics, mathematicians focusing on probability theory, and anyone interested in understanding the transformation of random variables.

WMDhamnekar
MHB
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Hello,

A discrete random variable X takes values $x_1,...,x_n$ each with probability $\frac1n$. Let Y=g(X) where g is an arbitrary real-valued function. I want to express the probability function of Y(pY(y)=P{Y=y}) in terms of g and the $x_i$
How can I answer this question?

If any member knows the correct answer, he/she may reply with correct answer.
 
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The notation Y(pY(y)=P{Y=y}) is confusing. For one, $Y$ accepts as argument elements of $\{x_1,\ldots.x_n\}$ and not equalities. if you need the probability mass function of $Y$, it is $$f_Y(y)=|g^{-1}(y)|/n$$. I don't think this can be simplified unless we know more about $g$.
 

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