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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.05062 |
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| _version_ | 1866912248871518208 |
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| author | Das, Abhranil |
| author_facet | Das, Abhranil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | New methods to compute the generalized chi-square distribution Das, Abhranil Computation Machine Learning Methodology 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. |
| title | New methods to compute the generalized chi-square distribution |
| topic | Computation Machine Learning Methodology |
| url | https://arxiv.org/abs/2404.05062 |