ArticleStatistics and computing2022
Optimal Bayesian design for model discrimination via classification.
Article in Statistics and computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data.PLoS computational biology · 2020Article
- A design criterion for symmetric model discrimination based on flexible nominal sets.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Identification of the relative timing of infectiousness and symptom onset for outbreak control.Journal of theoretical biology · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. This issue is compounded further when the likelihood functions for the rival models are computationally expensive. A new approach using supervised classification methods is developed to perform Bayesian optimal model discrimination design. This approach requires considerably fewer simulations from the candidate models than previous approaches using approximate Bayesian computation. Further, it is easy to assess the performance of the optimal design through the misclassification error rate. The approach is particularly useful in the presence of models with intractable likelihoods but can also provide computational advantages when the likelihoods are manageable. Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-022-10078-2.
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Registered trials
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