Documentation

Linglib.Core.Agent.NormalCDF

Standard Normal CDF Infrastructure #

@cite{luce-1959}

Shared Gaussian CDF definitions and properties used by both Thurstone's discriminal-process theory (§2.D) and Signal Detection Theory (§2.E) in @cite{luce-1959}.

Definitions #

Properties #

Monotonicity, strict monotonicity, symmetry, boundary values, and bounds. The CDF is defined via Mathlib's cdf (gaussianReal 0 1), which gives normalCDF x = (gaussianReal 0 1).real (Iic x). This grounds the properties in Mathlib's measure-theoretic Gaussian infrastructure.

noncomputable def Core.normalPDF (t : ) :

The standard normal probability density function: φ(t) = (1/√(2π)) · exp(-t²/2).

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    noncomputable def Core.normalCDF (x : ) :

    The standard normal cumulative distribution function: Φ(x) = ∫_{-∞}^{x} (1/√(2π)) exp(-t²/2) dt.

    Defined as cdf (gaussianReal 0 1) x, the CDF of the standard Gaussian measure evaluated at x.

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      The normal CDF is non-negative: Φ(x) ≥ 0 for all x.

      The normal CDF is at most 1: Φ(x) ≤ 1 for all x.

      The normal CDF is monotone increasing.

      theorem Core.normalCDF_neg (x : ) :

      Symmetry: Φ(-x) = 1 - Φ(x).

      Φ(0) = 1/2 by symmetry of the standard normal distribution.

      The normal CDF is strictly monotone increasing.

      theorem Core.normalCDF_pos_gt_half {x : } (hx : 0 < x) :
      1 / 2 < normalCDF x

      For x > 0, Φ(x) > 1/2.

      theorem Core.normalCDF_neg_lt_half {x : } (hx : x < 0) :
      normalCDF x < 1 / 2

      For x < 0, Φ(x) < 1/2.

      The normal CDF is injective (from strict monotonicity).

      The standard normal CDF is continuous.

      theorem Core.normalCDF_surj_Ioo (p : ) (hp0 : 0 < p) (hp1 : p < 1) :
      ∃ (x : ), normalCDF x = p

      The standard normal CDF is surjective onto (0, 1): for any p ∈ (0, 1), there exists x with Φ(x) = p.