2021/09/30 by Jinlin Lai, Lai, Jinlin, Justin Domke +3 · 1 citation
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2109.15134
Accepted to AISTATS 2022
openalex publication_date 2021/09/30 · arxiv created 2022/03/14 · arxiv updated 2022/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Variational inference for state space models (SSMs) is known to be hard in general. Recent works focus on deriving variational objectives for SSMs from unbiased sequential Monte Carlo estimators. We reveal that the marginal particle filter is obtained from sequential Monte Carlo by applying Rao-Blackwellization operations, which sacrifices the trajectory information for reduced variance and differentiability. We propose the variational marginal particle filter (VMPF), which is a differentiable and reparameterizable variational filtering objective for SSMs based on an unbiased estimator. We find that VMPF with biased gradients gives tighter bounds than previous objectives, and the unbiased reparameterization gradients are sometimes beneficial.