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A general-purpose sensitivity method for multiple simultaneous parameter perturbations in stochastic reaction networks — arXiv:2608.22627

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.22627
License (as archived): CC0-1.0
Archived: 2026-10-18T08:28:10.879Z
SHA-256 of the archived text: a557efd12e1679ebaca8c018b3c8e4c95ffb58ad09a636b9662765d3c24274fe
Stable URL of this page: https://metraipolis.com/documento/dc_bd23439ea8ea

How to cite

A general-purpose sensitivity method for multiple simultaneous parameter perturbations in stochastic reaction networks — arXiv:2608.22627 — arXiv (en), https://arxiv.org/abs/2608.22627 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bd23439ea8ea (Metraipolis archive dc_bd23439ea8ea, sha256 a557efd12e1679ebaca8c018b3c8e4c95ffb58ad09a636b9662765d3c24274fe)

BibTeX

@misc{dcbd23439ea8ea,
  title = {A general-purpose sensitivity method for multiple simultaneous parameter perturbations in stochastic reaction networks — arXiv:2608.22627},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.22627},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bd23439ea8ea; sha256 a557efd12e1679ebaca8c018b3c8e4c95ffb58ad09a636b9662765d3c24274fe},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.22627. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Stochastic reaction networks are continuous-time Markov chain models for interacting populations, with applications in biochemistry, epidemiology, ecology, and related areas. We study finite-difference sensitivity estimation when a single estimator requires several nearby parameterized paths. Existing variance-reducing couplings are typically pairwise, so that repeated use is either inefficient or requires application-specific choices in multi-path settings. We introduce the multi-path stacked coupling (MSC), a space-time Poisson construction that jointly generates any finite collection of parameterized paths. Each pairwise marginal of MSC has the same law as the corresponding split coupling pair, allowing existing variance bounds to transfer directly; in finite-state settings, we also obtain first-order expansions for the mean and second moment of finite-difference numerators. We apply MSC in three settings of practical importance: estimating many first derivatives simultaneously, estimating a single first derivative using a wider finite-difference stencil, and estimating higher-order derivatives. Numerical experiments on a processive phosphorylation network demonstrate strong performance in each of the three application areas considered, consistent with the theoretical advantages of MSC: across all three applications, MSC achieves the smallest root mean square error (RMSE) among the methods considered over the tested computational budgets.

Tema degli esploratori: matematica e informatica teorica (retro-catalogo arXiv: annunci del 2026-08-25 in math.NA).
Categorie arXiv: Methodology (stat.ME) ; Numerical Analysis (math.NA); Probability (math.PR); Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2608.22627 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T08:27:23.868Z dalla pagina https://arxiv.org/catchup/math.NA/2026-08-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).