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- W2626930952 abstract "Approximate Bayesian Computation (ABC) and other simulation-based inference methods are becoming increasingly used for inference in complex systems, due to their relative ease-of-implementation. We briefly review some of the more popular variants of ABC and their application in epidemiology, before using a real-world model of HIV transmission to illustrate some of challenges when applying ABC methods to high-dimensional, computationally intensive models. We then discuss an alternative approach—history matching—that aims to address some of these issues, and conclude with a comparison between these different methodologies." @default.
- W2626930952 created "2017-06-23" @default.
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- W2626930952 date "2018-02-01" @default.
- W2626930952 modified "2023-09-29" @default.
- W2626930952 title "Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models" @default.
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- W2626930952 doi "https://doi.org/10.1214/17-sts618" @default.
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