NeuralEstimators facilitates the user-friendly development of neural point estimators, which are neural networks that transform data into parameter point estimates. They are likelihood free, substantially faster than classical methods, and can be designed to be approximate Bayes estimators. The package caters for any model for which simulation is feasible. See the documentation to get started!
A convenient interface for R users is available here.
This software was developed as part of academic research. If you would like to support it, please star the repository. If you use it in your research or other activities, please also use the following citation.
@article{,
author = {Sainsbury-Dale, Matthew and Zammit-Mangion, Andrew and Huser, Raphaël},
title = {Likelihood-Free Parameter Estimation with Neural {B}ayes Estimators},
journal = {The American Statistician},
year = {2024},
volume = {78},
pages = {1--14},
doi = {10.1080/00031305.2023.2249522},
url = {https://doi.org/10.1080/00031305.2023.2249522}
}
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Likelihood-free parameter estimation with neural Bayes estimators [paper] [code]
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Neural Bayes estimators for censored inference with peaks-over-threshold models [paper]
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Neural Bayes estimators for irregular spatial data using graph neural networks [paper][code]
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Modern extreme value statistics for Utopian extremes [paper]
Several other software packages have been developed to facilitate neural likelihood-free inference. These include:
A summary of the functionality in these packages is given in Zammit-Mangion et al. (2024, Section 6.1). Note that this list of related packages was created in July 2024; if you have software to add to this list, please contact the package maintainer.