On the efficient determination of optimal Bayesian experimental designs using ABC: a case study in optimal observation of epidemics

DOI10.1016/j.jspi.2015.12.008zbMath1334.62198OpenAlexW2226294016WikidataQ62563941 ScholiaQ62563941MaRDI QIDQ256457

Joshua V. Ross, Jonathan Tuke, David J. Price, Nigel G. Bean

Publication date: 9 March 2016

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2440/99437

zbMATH Keywords

approximate Bayesian computationBayesian optimal designstochastic epidemic models

Mathematics Subject Classification ID

Epidemiology (92D30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Optimal statistical designs (62K05) Bayesian inference (62F15)

Related Items (5)

Optimal Bayesian design for model discrimination via classification ⋮ Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology ⋮ An induced natural selection heuristic for finding optimal Bayesian experimental designs ⋮ Designing group dose-response studies in the presence of transmission ⋮ Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation

Uses Software

• R
• agricolae

Cites Work

• Unnamed Item
• Novel moment closure approximations in stochastic epidemics
• Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models
• Kernel density estimation via diffusion
• Qualitative behavior of stochastic epidemics
• Bayesian experimental design: A review
• Bayesian statistics and the efficiency and ethics of clinical trials
• Optimal design of experimental epidemics
• Optimal Observation Times in Experimental Epidemic Processes
• Constructing Summary Statistics for Approximate Bayesian Computation: Semi-Automatic Approximate Bayesian Computation
• Bayesian Experimental Design for Models with Intractable Likelihoods
• On Information and Sufficiency

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