acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange

Autor: Antony M. Overstall, David C. Woods, Maria Adamou

Publikováno v: Journal of Statistical Software, Vol 95, Iss 1, Pp 1-33 (2020)

We describe the R package acebayes and demonstrate its use to find Bayesian optimal experimental designs. A decision-theoretic approach is adopted, with the optimal design maximizing an expected utility. Finding Bayesian optimal designs for realistic

Externí odkaz: https://doaj.org/article/f530bb8a94544f959adb3d6349231277

Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models

Autor: Yiolanda Englezou, Timothy W. Waite, David C. Woods

Publikováno v: Englezou, Y, Waite, T & Woods, D C 2022, ' Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models ', Statistics and Computing, vol. 32, 82 . https://doi.org/10.1007/s11222-022-10159-2

One of the major challenges in Bayesian optimal design is to approximate the expected utility function in an accurate and computationally efficient manner. We focus on Shannon information gain, one of the most widely used utilities when the experimen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b6179618a17a437fe3a9bc3632bb10a
https://eprints.soton.ac.uk/470495/

acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange

Autor: Maria Adamou, David C. Woods, Antony M. Overstall

Publikováno v: Journal of Statistical Software, Vol 95, Iss 1, Pp 1-33 (2020)
Journal of Statistical Software; Vol 95 (2020); 1-33

We describe the R package acebayes and demonstrate its use to find Bayesian optimal experimental designs. A decision-theoretic approach is adopted, with the optimal design maximizing an expected utility. Finding Bayesian optimal designs for realistic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b727cecdcb44c9220f903e7fa3cd933
https://www.jstatsoft.org/index.php/jss/article/view/3137

Multi-fidelity Statistical Machine Learning for Molecular Crystal Structure Prediction

Autor: Olga Egorova, Graeme M. Day, Roohollah Hafizi, David C. Woods

The prediction of crystal structures from first principles requires highly accurate energies for large numbers of putative crystal structures. The accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2712953e50e002e6f33cf6889ca312c0
https://doi.org/10.26434/chemrxiv.12407831

Bayesian optimal design for ordinary differential equation models with application in biological science

Autor: Ben M. Parker, Antony M. Overstall, David C. Woods

Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of non-linear ordinary differential equations. The motivation is an experiment to estimate parameters in the equations governing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbf8d52e04c711b8e53eaf48c645528a
https://repository.uwl.ac.uk/id/eprint/6177/1/Parker_Woods_Overstall_JASA_2019_Bayesian_optimal_design_for_ordinary_differential_equation_models_with_application_in_biological_science.pdf