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UID:pretalx-scipy-2026-BFQAPR@pretalx.com
DTSTART;TZID=CST:20260715T143500
DTEND;TZID=CST:20260715T150500
DESCRIPTION:Many scientific workflows rely on derivatives of complex models
 : Fisher forecasts\, sensitivity analysis\, gradient-based inference\, and
  emulator construction. In practice\, these derivatives are often difficul
 t to compute reliably and integrate into end-to-end inference pipelines.\n
 \nDerivKit is an open-source Python toolkit that provides a unified framew
 ork for derivative-based scientific inference. It supports multiple deriva
 tive backends and connects model evaluation directly to downstream inferen
 ce tools\, including Fisher analyses and higher-order likelihood approxima
 tions. The framework also provides diagnostics and visualization tools for
  exploring parameter sensitivities and degeneracies.\n\nOriginally develop
 ed for cosmological forecasting pipelines\, DerivKit is designed to be dom
 ain-agnostic and easily integrated into scientific Python workflows.
DTSTAMP:20260715T022642Z
LOCATION:University Hall
SUMMARY:DerivKit: End-to-End Derivative-Based Inference in Scientific Pytho
 n - Niko Sarcevic\, Matthijs van der Wild
URL:https://pretalx.com/scipy-2026/talk/BFQAPR/
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