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Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation

arXiv 2312.09860 ↗First posted 15 Dec 2023cs.LG stat.COcited by 0

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Abstract (arXiv)

Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models. In this paper, we propose a mixed inference algorithm that computes closed-form solutions using belief propagation as much as possible, and falls back to sampling-based SMC methods when exact computations fail. This algorithm thus implements automatic Rao-Blackwellization and is even exact for Gaussian tree models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 16 references that are in the map. First posted on arXiv 15 Dec 2023.

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7 of 16 references.

References · 7

  1. 2018Pyro: Deep Universal Probabilistic Programmingcited by 380
  2. 2019A Differentiable Programming System to Bridge Machine Learning and Scientific Computingcited by 83
  3. 2016Design and Implementation of Probabilistic Programming Language Anglicancited by 15
  4. 2018Automated learning with a probabilistic programming language: Birchcited by 11
  5. 2017Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programscited by 8
  6. 2019Reactive Probabilistic Programmingcited by 6
  7. 2022Semi-Symbolic Inference for Efficient Streaming Probabilistic Programmingcited by 5

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