Multi-Model Variational Bayesian Approaches for Causality Analysis

  • Author / Creator
    Prabhakaran, Aswathi
  • Causality analysis using data-driven models helps in the construction of graphical models
    that illustrate the interaction among the variables of a process system. A majority of
    industrial processes operate in multiple operating modes and thus the measurements from
    these processes exhibit multi-modal characteristics. However, the literature for causality
    analysis is skewed towards analyzing unimodal processes. In this work, we propose an
    approach for causality analysis in multi-modal systems.\par
    Granger causality analysis is one of the widely popular methods for causality analysis.
    Classical techniques for multivariate Granger causality analysis rely on significance tests
    on parameters of vector autoregressive (VAR) models or vector moving average (VMA)
    models of the actual unimodal processes. In this work, we propose a Granger causality analysis
    technique with multi-modal VAR models. Our technique relies on variational Bayesian
    analysis of multi-modal VAR models. It imposes a soft constraint through Normal-Gamma
    priors on multi-modal VAR model parameters. This soft constraint ensures that the causal
    graphs extracted from different modes are consistent while allowing the strengths of
    interaction to vary across modes. Our approach also provides a single metric to assess
    the significance of each causal interaction in multi-modal systems. We illustrate the
    proposed algorithm using both simulation and industrial data. Furthermore, Bayesian network based approach for Granger causality analysis in multi-mode systems can handle data with outliers. The performance of the robust method is also tested using simulation and industrial process data.

  • Subjects / Keywords
  • Graduation date
    Spring 2021
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.