Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics

DOI10.1016/j.csda.2018.10.013OpenAlexW2963413693MaRDI QIDQ1727860

David C. Woods, Kieran J. Martin, Antony M. Overstall

Publication date: 21 February 2019

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1602.01347

zbMATH Keywords

Gibbs samplingparallel temperingmultivariate Gaussian processapproximate coordinate exchangeRiemann manifold Langevin Metropolis-Hastings

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (1)

Special issue on biostatistics

Uses Software

• R
• VODE
• deSolve
• fields
• BayesDA
• diffEq
• acebayes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian emulation of complex multi-output and dynamic computer models
• The design and analysis of computer experiments.
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Process optimization. A statistical approach.
• Bayesian Calibration of Computer Models
• A Strategy for Bayesian Inference for Computationally Expensive Models with Application to the Estimation of Stem Cell Properties
• A First Course in the Numerical Analysis of Differential Equations
• VODE: A Variable-Coefficient ODE Solver
• Some matrix-variate distribution theory: Notational considerations and a Bayesian application
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Optimization

This page was built for publication: Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics