Beyond Bayesian model averaging over paths in probabilistic programs with stochastic support
The posterior in probabilistic programs with stochastic support decomposes as a weighted sum of the local posterior distributions associated with each possible program path. We show that making predictions with this full posterior implicitly performs a Bayesian model averaging (BMA) over paths. This...
| Главные авторы: | , , |
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| Формат: | Conference item |
| Язык: | English |
| Опубликовано: |
Journal of Machine Learning Research
2024
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