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These systems use heuristics to partition random variables within the program into variables that are encoded symbolically and variables that are encoded with sampled values, and the heuristics are not necessarily aligned with the developer\u2019s performance evaluation metrics. In this work, we present\n                    <jats:italic toggle=\"yes\">inference plans<\/jats:italic>\n                    , a programming interface that enables developers to control the partitioning of random variables during hybrid particle filtering. We further present\n                    <jats:sc>Siren<\/jats:sc>\n                    , a new PPL that enables developers to use annotations to specify inference plans the inference system must implement. To assist developers with statically reasoning about whether an inference plan can be implemented, we present an abstract-interpretation-based static analysis for\n                    <jats:sc>Siren<\/jats:sc>\n                    for determining inference plan\n                    <jats:italic toggle=\"yes\">satisfiability<\/jats:italic>\n                    . We prove the analysis is sound with respect to\n                    <jats:sc>Siren<\/jats:sc>\n                    \u2019s semantics. Our evaluation applies inference plans to three different hybrid particle filtering algorithms on a suite of benchmarks. It shows that the control provided by inference plans enables speed ups of 1.76 x on average and up to 206 x to reach a target accuracy, compared to the inference plans implemented by default heuristics; the results also show that inference plans improve accuracy by 1.83 x on average and up to 595 x with less or equal runtime, compared to the default inference plans. We further show that our static analysis is precise in practice, identifying all satisfiable inference plans in 27 out of the 33 benchmark-algorithm evaluation settings.\n                  <\/jats:p>","DOI":"10.1145\/3704846","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T05:48:42Z","timestamp":1736401722000},"page":"271-299","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Inference Plans for Hybrid Particle Filtering"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6128-0351","authenticated-orcid":false,"given":"Ellie Y.","family":"Cheng","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8396-1258","authenticated-orcid":false,"given":"Eric","family":"Atkinson","sequence":"additional","affiliation":[{"name":"Binghamton University, Binghamton, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2230-1616","authenticated-orcid":false,"given":"Guillaume","family":"Baudart","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9 - CNRS - Inria - IRIF, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5291-6067","authenticated-orcid":false,"given":"Louis","family":"Mandel","sequence":"additional","affiliation":[{"name":"IBM, Yorktown Heights, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6928-0456","authenticated-orcid":false,"given":"Michael","family":"Carbin","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"e_1_3_2_2_1","article-title":"A causal factors analysis of aircraft incidents due to radar limitations: The Norway case study","volume":"44","author":"Syd Ali Busyairah","year":"2015","unstructured":"Busyairah Syd Ali, Arnab Majumdar, Washington Yotto Ochieng, Wolfgang Schuster, and Thiam Kian Chiew. 2015. 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