Help with simulating distributions

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SUMMARY

This discussion focuses on simulating distributions using cumulative distribution functions (c.d.f.s) and their corresponding transformations. The participants analyze three specific c.d.f.s: F(x) = x for 0 ≤ x ≤ 1, F(x) = x² for 0 ≤ x ≤ 1, and F(x) = (x²)/9 for 0 ≤ x ≤ 3. The solution involves deriving the inverse of these c.d.f.s to express X in terms of U, where U follows a Uniform[0,1] distribution. The method of using the minimum function Y = min(x: F(x) ≥ u) is discussed as a potential approach for simulation.

PREREQUISITES
  • Understanding of cumulative distribution functions (c.d.f.s)
  • Knowledge of probability theory, specifically uniform distributions
  • Familiarity with inverse functions and transformations in statistics
  • Basic calculus for differentiation of functions
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  • Study the concept of inverse transform sampling in probability theory
  • Learn about the properties and applications of cumulative distribution functions
  • Explore the derivation of probability density functions (pdfs) from c.d.f.s
  • Investigate numerical methods for simulating random variables from specified distributions
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Help with simulating distributions...

Homework Statement



For each of the following c.d.f F, find a formula for X in terms of U, such that if U~Uniform[0,1], then X has c.d.f F.

a)
F(x) =
0 if 0 x<0
x if 0<=x<=1
1 if x>1
b)
F(x) =
0 if 0 x<0
x^2 if 0<=x<=1
1 if x>1
c)
F(x) =
0 if 0 x<0
(x^2)/9 if 0<=x<=3
1 if x>3

How do I solve these?

Homework Equations





The Attempt at a Solution


a)
Is it
Y=min(x:F(x)>=u)
Do i need to simplify this more?
 
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I would start by differentiating the cdf F(x) to get the pdf f(x).
 

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