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Classification framework for partially observed dynamical systems.

Yuan Shen1, Peter Tino2, Krasimira Tsaneva-Atanasova3

  • 1School of Computer Science, The University of Birmingham, Birmingham, United Kingdom and Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China.

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Summary
This summary is machine-generated.

We developed a new framework for classifying partially observed dynamical systems using posterior distributions to handle uncertainty. This approach effectively classifies systems even with simplified models, improving machine learning for complex data.

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Area of Science:

  • Dynamical Systems
  • Machine Learning
  • Statistical Inference

Background:

  • Partially observed dynamical systems present classification challenges due to inherent uncertainties.
  • Current methods often rely on point estimates, neglecting crucial uncertainty quantification.

Purpose of the Study:

  • To introduce a general framework for classifying partially observed dynamical systems.
  • To incorporate uncertainty from generative and observational processes using posterior distributions.

Main Methods:

  • Learning in the model space using posterior distributions over model parameters.
  • Evaluation on a biological pathway model and a stochastic double-well system.

Main Results:

  • The framework effectively classifies partially observed dynamical systems.
  • Classification performance is maintained with simplified inferential models that capture essential characteristics.
  • Uncertainty from noise and sampling is principledly handled.

Conclusions:

  • The proposed framework offers a robust method for classifying dynamical systems.
  • Simplified inferential models can be sufficient for classification tasks, enhancing practical applicability.
  • This approach advances the understanding and application of machine learning in complex systems.