New methods to compute the generalized chi-square distribution
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Abstract (arXiv)
We present four new mathematical methods, two exact and two approximate, along with open-source software, to compute the cdf, pdf and inverse cdf of the generalized chi-square distribution. Some methods are geared for speed, while others are designed to be accurate far into the tails, using which we can also measure large values of the discriminability index d' between multivariate normal distributions. We compare the accuracy and speed of these and previous methods, characterize their advantages and limitations, and identify the best methods to use in different cases.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 50 references that are in the map. First posted on arXiv 7 Apr 2024.
Builds on
6 of 50 references.
References · 6
- 2020Methods to integrate multinormals and compute classification measurescited by 3
- 2022Provable Probabilistic Safety and Feasibility-Assured Control for Autonomous Vehicles using Exponential Control Barrier Functionscited by 2
- 2020An efficient and accurate approximation to the distribution of quadratic forms of Gaussian variablescited by 1
- 2022Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximationscited by 1
- 2023Purposeful Co-Design of OFDM Signals for Ranging and Communicationscited by 1
- 2023Exact Consistency Tests for Gaussian Mixture Filters using Normalized Deviation Squared Statisticscited by 1
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Citing papers per year: 2026: 1.
1 paper.