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Streaming Variational Monte Carlo.

ABSTRACT: Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. We develop a novel online learning framework, leveraging variational inference and sequential Monte Carlo, which enables flexible and accurate Bayesian joint filtering. Our method provides an approximation of the filtering posterior which can be made arbitrarily close to the true filtering distribution for a wide class of dynamics models and observation models. Specifically, the proposed framework can efficiently approximate a posterior over the dynamics using sparse Gaussian processes, allowing for an interpretable model of the latent dynamics. Constant time complexity per sample makes our approach amenable to online learning scenarios and suitable for real-time applications.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC10082974 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Streaming Variational Monte Carlo.

Zhao Yuan Y Nassar Josue J Jordan Ian I Bugallo Monica M Park Il Memming IM

IEEE transactions on pattern analysis and machine intelligence 20221205 1

Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. We develop a novel online learning framework, leveraging variational inference and sequential Monte Carlo, which enables flexible and accurate Bayesian joint filtering. Our method provides an approximation of the fil ...[more]

PMID: 35201981

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