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[Instant NGP Code Digest - A First Principle Perspective] CDF: Cumulative Distribution Function
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  • Implementation / Usage in Instant NGP (moved to here)
  • CDF Definition (2025-11-27)

1. What is CDF?#

Definition#

The cumulative distribution function (CDF) of a real-valued random variable XX is the function given by:[1]

FX(x)=P(Xx)F_X(x) = \mathbb{P}(X \le x)

where the right-hand side represents the probability that the random variable XX takes on a value less than or equal to xx.

The probability that XX lies in the semi-closed interval (a,b](a, b], where a<ba < b, is therefore:[1]

P(a<Xb)=FX(b)FX(a)\mathbb{P}(a < X \le b) = F_X(b) - F_X(a)

The CDF of a continuous random variable XX can be expressed as the integral of its probability density function (PDF) fXf_X as follows:[1]

FX(x)=xfX(t)dtF_X(x) = \int_{-\infty}^{x} f_X(t)\, dt

CDF for the normal distribution N(μ,σ2){\mathcal{N}}(\mu ,\sigma ^{2})#

CDF

Reference#

  1. https://en.wikipedia.org/wiki/Cumulative_distribution_function

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