Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis

DOI10.1016/j.jcp.2020.109999OpenAlexW3100322616WikidataQ115350090 ScholiaQ115350090MaRDI QIDQ2120013

Dirk Husmeier, Diana Giurghita, Agnieszka Borowska

Publication date: 31 March 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: http://eprints.gla.ac.uk/226089/3/226089.pdf

zbMATH Keywords

Gaussian processesstochastic differential equationschemotaxisstatistical inferencebiophysicsapproximate Bayesian computations

Mathematics Subject Classification ID

Statistics (62-XX) Parametric inference (62Fxx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (1)

Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

Uses Software

• R
• Chemotaxis
• Momocs
• EasyABC

Cites Work

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