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Message Passing-Based Inference for Time-Varying Autoregressive Models.

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  • 1Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.

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Summary
This summary is machine-generated.

This study introduces a novel factor graph approach for online adaptation in time-varying autoregressive (TVAR) models, overcoming key challenges in state and parameter inference for non-stationary signals.

Keywords:
Bayesian inferencefactor graphfree energyhybrid message passingmodel selectionnon-stationary systemsprobabilistic graphical models

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Area of Science:

  • Signal Processing
  • Statistical Modeling
  • Machine Learning

Background:

  • Time-varying autoregressive (TVAR) models are crucial for analyzing non-stationary signals.
  • Online joint adaptation of states and parameters in TVAR models presents significant challenges.

Purpose of the Study:

  • To develop an efficient method for online joint adaptation of states and parameters in TVAR models.
  • To introduce a factor graph representation for TVAR models to facilitate inference.

Main Methods:

  • Representing TVAR models using factor graphs.
  • Employing automated message passing for state and parameter inference.
  • Deriving structured variational update rules for composite AR nodes.
  • Utilizing variational free energy (FE) for performance tracking.

Main Results:

  • Successfully adapted states and parameters in TVAR models online.
  • Demonstrated the utility of the composite AR node as a plug-in module in hierarchical models.
  • Validated the method on synthetic data and real-world applications.

Conclusions:

  • The proposed factor graph and message passing approach effectively addresses online adaptation challenges in TVAR models.
  • The method provides a robust Bayesian measure of TVAR model performance via variational free energy.
  • The approach is validated for temperature modeling and speech enhancement.