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  Bayesian Information Criterion for Event-based Multi-trial Ensemble data

Shao, K., Logothetis, N., & Besserve, M. (submitted). Bayesian Information Criterion for Event-based Multi-trial Ensemble data.

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https://arxiv.org/pdf/2204.14096 (Any fulltext)
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 Creators:
Shao, K1, Author              
Logothetis, NK1, Author              
Besserve, M, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 Abstract: Transient recurring phenomena are ubiquitous in many scientific fields like neuroscience and meteorology. Time inhomogenous Vector Autoregressive Models (VAR) may be used to characterize peri-event system dynamics associated with such phenomena, and can be learned by exploiting multi-dimensional data gathering samples of the evolution of the system in multiple time windows comprising, each associated with one occurrence of the transient phenomenon, that we will call "trial". However, optimal VAR model order selection methods, commonly relying on the Akaike or Bayesian Information Criteria (AIC/BIC), are typically not designed for multi-trial data. Here we derive the BIC methods for multi-trial ensemble data which are gathered after the detection of the events. We show using simulated bivariate AR models that the multi-trial BIC is able to recover the real model order. We also demonstrate with simulated transient events and real data that the multi-trial BIC is able to estimate a sufficiently small model order for dynamic system modeling.

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 Dates: 2022-04
 Publication Status: Submitted
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