Approximate Bayesian computation with path signatures
Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation generates likelihood-free posterior samples by comparing simulated and observed data through some distance measure, but existing approaches are often...
| 主要な著者: | , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
Proceedings of Machine Learning Research
2024
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