SUMMARY
The cumulative distribution function (CDF) for a uniform distribution on a circle of radius R can be expressed in Cartesian coordinates as F(x,y) = Prob((X < x) and (Y < y)). The probability density function (PDF) is defined as f(x,y) = δ(√(x² + y²) - R) / (2πR), where δ is the Dirac delta function. The CDF represents the fraction of the circle that lies below and to the left of a given point (x,y). Specific values include F(0,0) = 1/4 and F(1,0) = F(0,1) = 1/2 for a unit circle.
PREREQUISITES
- Understanding of probability density functions (PDFs) and cumulative distribution functions (CDFs)
- Familiarity with Dirac delta function and its applications
- Knowledge of polar and Cartesian coordinate systems
- Basic concepts of measure theory, particularly Radon-Nikodym derivatives
NEXT STEPS
- Study the properties of the Dirac delta function in probability theory
- Learn about Radon-Nikodym derivatives and their applications in measure theory
- Explore the relationship between polar and Cartesian coordinates in probability distributions
- Investigate the geometric interpretation of cumulative distribution functions in various distributions
USEFUL FOR
Mathematicians, statisticians, and data scientists interested in probability theory, particularly those working with geometric distributions and measure theory.