Inference Plans for Hybrid Particle Filtering

Inference Plans for Hybrid Particle Filtering

Advanced probabilistic programming languages (PPLs) using hybrid particle filtering combine symbolic exact inference and Monte Carlo methods to improve inference performance. These systems use heuristics to partition random variables within the program into variables that are encoded symbolically an...

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Bibliographic Details
Main Authors: Cheng, Ellie, Atkinson, Eric, Baudart, Guillaume, Mandel, Louis, Carbin, Michael
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: Association for Computing Machinery 2025
Online Access:https://hdl.handle.net/1721.1/158236

Internet

https://hdl.handle.net/1721.1/158236

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