Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models

DOI10.3150/16-BEJ856zbMath1459.62048arXiv1411.1166WikidataQ57424147 ScholiaQ57424147MaRDI QIDQ2405165

Prithwish Bhaumik, Subhashis Ghosal

Publication date: 21 September 2017

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1411.1166

zbMATH Keywords

ordinary differential equationBernstein-von Mises theoremRunge-Kutta methodBayesian inferenceapproximate likelihoodspline smoothing

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic distribution theory in statistics (62E20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

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Rates and coverage for monotone densities using projection-posterior ⋮ Two-step Bayesian methods for generalized regression driven by partial differential equations ⋮ Adaptive Semiparametric Bayesian Differential Equations Via Sequential Monte Carlo ⋮ Bayesian smooth‐and‐match inference for ordinary differential equations models linear in the parameters ⋮ Coverage of credible intervals in Bayesian multivariate isotonic regression ⋮ Coverage of credible intervals in nonparametric monotone regression ⋮ Bayesian analysis of mixed-effect regression models driven by ordinary differential equations ⋮ Convergence rates for Bayesian estimation and testing in monotone regression

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