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- W4289367619 abstract "Parametric statistical models that are implicitly defined in terms of a stochastic data generating process are used in a wide range of scientific disciplines because they enable accurate modeling. However, learning the parameters from observed data is generally very difficult because their likelihood function is typically intractable. Likelihood-free Bayesian inference methods have been proposed which include the frameworks of approximate Bayesian computation (ABC), synthetic likelihood, and its recent generalization that performs likelihood-free inference by ratio estimation (LFIRE). A major difficulty in all these methods is choosing summary statistics that reduce the dimensionality of the data to facilitate inference. While several methods for choosing summary statistics have been proposed for ABC, the literature for synthetic likelihood and LFIRE is very thin to date. We here address this gap in the literature, focusing on the important special case of time-series models. We show that convolutional neural networks trained to predict the input parameters from the data provide suitable summary statistics for LFIRE. On a wide range of time-series models, a single neural network architecture produced equally or more accurate posteriors than alternative methods." @default.
- W4289367619 created "2022-08-02" @default.
- W4289367619 creator A5038807284 @default.
- W4289367619 creator A5083146236 @default.
- W4289367619 date "2018-10-23" @default.
- W4289367619 modified "2023-09-23" @default.
- W4289367619 title "Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)" @default.
- W4289367619 doi "https://doi.org/10.48550/arxiv.1810.09899" @default.
- W4289367619 hasPublicationYear "2018" @default.
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