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October 3, 2025Open Access

Canonical Bayesian Linear System Identification

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Authors

ABAndrey BryutkinMLMatthew E. LevineBroad InstituteIUIñigo UrteagaIkerbasque

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Overview

Novel approach resolves parameter non-identifiability in time-invariant systems, indicating improved inference accuracy and robust uncertainty estimates.

Key Points

  • The new framework allows for efficient inference in linear time-invariant systems, addressing previous identifiability issues.
  • By utilizing canonical forms, the method captures all invariant dynamics like transfer functions and eigenvalues.
  • The approach ensures stability conditions through structure-aware priors, enhancing predictive distribution reliability.
  • Simulations demonstrate that the new method with MCMC outperforms standard parameterization methods, particularly in uncertainty estimation.

Cite This Study

Bryutkin et al. (2025) studied this question.

synapsesocial.com/papers/68e02f3cf0e39f13e7fa24achttps://doi.org/10.48550/arxiv.2507.11535
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