Characteristic Function of the Tsallis $q$-Gaussian and Its Applications in Measurement and Metrology

Paper: arXiv 2303.08615

Authors: Viktor Witkovský

Abstract

The Tsallis $q$-Gaussian distribution is a powerful generalization of the standard Gaussian distribution and is commonly used in various fields, including non-extensive statistical mechanics, financial markets and image processing. It belongs to the $q$-distribution family, which is characterized by a non-additive entropy. Due to their versatility and practicality, $q$-Gaussians are a natural choice for modeling input quantities in measurement models. This paper presents the characteristic function of a linear combination of independent $q$-Gaussian random variables and proposes a numerical method for its inversion. The proposed technique makes it possible to determine the exact probability distribution of the output quantity in linear measurement models, with the input quantities modeled as independent $q$-Gaussian random variables. It provides an alternative computational procedure to the Monte Carlo method for uncertainty analysis through the propagation of distributions.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a rigorous mathematical derivation of the characteristic function for linear combinations of Tsallis q-Gaussian distributions and a corresponding inversion algorithm. However, its focus is largely theoretical and metrological, lacking empirical financial backtesting or real-world market data validation.

Research Flowchart

  flowchart TD
    A[Goal: Exact Uncertainty Analysis via Tsallis q-Gaussian Distributions] --> B[Inputs: Linear Measurement Model & Independent q-Gaussian Inputs]
    B --> C[Derive Characteristic Function of Linear Combination of q-Gaussians]
    C --> D[Apply Numerical Fourier Inversion Method]
    D --> E[Outcome: Exact PDF/CDF of Output Quantity without Monte Carlo Simulations]