This letter introduces a new approach to the prob- lem of approximating the probability density function (PDF) and the cumulative distribution function (CDF) of a positive random variable. The novel approximation strategy is based on the analysis of a suitably defined sequence of auxiliary variables which converges in distribution to the target variable. By leveraging such convergence, simple approximations for both the CDF and PDF of the target variable are given in terms of the derivatives of its moment generating function (MGF). In contrast to classical approximation methods based on truncated series of moments or cumulants, our approximations always represent a valid distribution and the relative error between variables is independent of the variable under analysis. The derived results are then used to approximate the statistics of positive-definite real Gaussian quadratic forms, comparing our proposed approach with other existing approximations in the literature.
Ramirez-Espinosa, P., Morales-Jimenez, D., Cortés, J. A., Paris, J. F., & Martos-Naya, E. (2019). New Approximation to Distribution of Positive RVs applied to Gaussian Quadratic Forms. IEEE Signal processing Letters, 26(6), 923-927. https://doi.org/10.1109/LSP.2019.2912295