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

  • Multi-Model
  • Variational Bayesian
  • Causality Analysis

• Graduation date

  Spring 2021

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-wacj-t463

• 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.