DerivKit: stable numerical derivatives bridging Fisher forecasts and MCMC
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910015654199296 |
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| author | Šarčević, Nikolina van der Wild, Matthijs Trendafilova, Cynthia |
| author_facet | Šarčević, Nikolina van der Wild, Matthijs Trendafilova, Cynthia |
| contents | DerivKit is a Python package for derivative-based statistical inference. It implements stable numerical differentiation and derivative assembly utilities for Fisher-matrix forecasting and higher-order likelihood approximations in scientific applications, supporting scalar- and vector-valued models including black-box or tabulated functions where automatic differentiation is impractical or unavailable. These derivatives are used to construct Fisher forecasts, Fisher bias estimates, and non-Gaussian likelihood expansions based on the Derivative Approximation for Likelihoods (DALI). By extending derivative-based inference beyond the Gaussian approximation, DerivKit forms a practical bridge between fast Fisher forecasts and more computationally intensive sampling-based methods such as Markov chain Monte Carlo (MCMC). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08078 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DerivKit: stable numerical derivatives bridging Fisher forecasts and MCMC Šarčević, Nikolina van der Wild, Matthijs Trendafilova, Cynthia Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Data Analysis, Statistics and Probability DerivKit is a Python package for derivative-based statistical inference. It implements stable numerical differentiation and derivative assembly utilities for Fisher-matrix forecasting and higher-order likelihood approximations in scientific applications, supporting scalar- and vector-valued models including black-box or tabulated functions where automatic differentiation is impractical or unavailable. These derivatives are used to construct Fisher forecasts, Fisher bias estimates, and non-Gaussian likelihood expansions based on the Derivative Approximation for Likelihoods (DALI). By extending derivative-based inference beyond the Gaussian approximation, DerivKit forms a practical bridge between fast Fisher forecasts and more computationally intensive sampling-based methods such as Markov chain Monte Carlo (MCMC). |
| title | DerivKit: stable numerical derivatives bridging Fisher forecasts and MCMC |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2602.08078 |