An Extendable Python Implementation of Robust Optimization Monte Carlo
Performing inference in statistical models with an intractable likelihood is challenging, therefore, most likelihood-free inference (LFI) methods encounter accuracy and efficiency limitations. In this paper, we present the implementation of the LFI method robust optimization Monte Carlo (ROMC) in t...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2024-08-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4678 |
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