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- W2905463339 abstract "Probabilistic programming is a programming paradigm for expressing flexible probabilistic models. Implementations of probabilistic programming languages employ a variety of inference algorithms, where sequential Monte Carlo methods are commonly used. A problem with current state-of-the-art implementations using sequential Monte Carlo inference is the alignment of program synchronization points. We propose a new static analysis approach based on the 0-CFA algorithm for automatically aligning higher-order probabilistic programs. We evaluate the automatic alignment on a phylogenetic model, showing a significant decrease in runtime and increase in accuracy." @default.
- W2905463339 created "2018-12-22" @default.
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- W2905463339 date "2018-12-18" @default.
- W2905463339 modified "2023-09-27" @default.
- W2905463339 title "Automatic Alignment of Sequential Monte Carlo Inference in Higher-Order Probabilistic Programs." @default.
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