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

arXiv: https://arxiv.org/abs/2204.14096 · HTML: https://mlsanity.com/paper/2204.14096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Telling cause from effect in deterministic linear dynamical systems](https://mlsanity.com/paper/1503.01299/) (2015)
- [Multiscale Bayesian State Space Model for Granger Causality Analysis of Brain Signal](https://mlsanity.com/paper/1704.02778/) (2017)

## Built on by (most cited first, 1 of 1)

- [Information Theoretic Measures of Causal Influences during Transient Neural Events](https://mlsanity.com/paper/2209.07508/) (2022)

## Abstract (arXiv)

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.