Towards Bayesian experimental design for nonlinear models that require a large number of sampling times
DOI10.1016/J.CSDA.2013.08.017zbMath1471.62175OpenAlexW1986548506WikidataQ62899403 ScholiaQ62899403MaRDI QIDQ1615210
M. Helen Thompson, Elizabeth G. Ryan, Christopher C. Drovandi, Anthony N. Pettitt
Publication date: 2 November 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://eprints.qut.edu.au/56522/4/56522.pdf
Markov chain Monte Carlostochastic optimisationrobust designBayesian optimal designsampling strategies
Computational methods for problems pertaining to statistics (62-08) Optimal statistical designs (62K05)
Related Items (14)
Cites Work
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- Optimal weights for experimental designs on linearly independent support points
- Incorporating prior parameter uncertainty in the design of sampling schedules for pharmacokinetic parameter estimation experiments
- Optimal Bayesian design applied to logistic regression experiments
- Bayesian experimental design: A review
- Multivariate plug-in bandwidth selection
- Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data
- Decision Analysis by Augmented Probability Simulation
- Optimal Sampling Times in Population Pharmacokinetic Studies
- Optimal Observation Times in Experimental Epidemic Processes
- Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science
- Marginal Likelihood Estimation via Power Posteriors
- Bayesian Experimental Design for Models with Intractable Likelihoods
- Bayesian-Optimal Design via Interacting Particle Systems
- On Information and Sufficiency
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