GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation | Zendy

Evgeny Tankhilevich | Zendy; Jonathan Ish-Horowicz | Zendy; Tara Hameed | Zendy; Elisabeth Roesch | Zendy; Istvan T. Kleijn | Zendy; Michael P. H. Stumpf | Zendy; Fei He | Zendy

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GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

Author(s) -

Evgeny Tankhilevich,

Jonathan Ish-Horowicz,

Tara Hameed,

Elisabeth Roesch,

Istvan T. Kleijn,

Michael P. H. Stumpf,

Fei He

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa078

Subject(s) - approximate bayesian computation , emulation , computer science , computation , algorithm , gaussian process , inference , monte carlo method , bayesian probability , bayesian inference , markov chain monte carlo , gaussian , mathematical optimization , theoretical computer science , artificial intelligence , mathematics , statistics , physics , quantum mechanics , economics , economic growth

Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice.

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