Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Graphical models are powerful tools that are regularly used to investigate complex
dependence structures in high-throughput biomedical datasets. They allow for holistic …

Human brain effective connectivity characterizes the causal effects of neural activities
among different brain regions. Studies of brain effective connectivity networks (ECNs) for …

A general framework for modeling dependence in multivariate time series is presented. Its
fundamental approach relies on decomposing each signal inside a system into various …

In this article, we develop an analytical approach for estimating brain connectivity networks
that accounts for subject heterogeneity. More specifically, we consider a novel extension of a …

Brain network interactions are commonly assessed via functional (network) connectivity,
captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity …

Understanding how one brain region exerts influence over another in vivo is profoundly
constrained by models used to infer or predict directed connectivity. Although such neural …

In this paper we propose BVAR-connect, a variational inference approach to a Bayesian
multi-subject vector autoregressive (VAR) model for inference on effective brain connectivity …

Neuroscientific studies aim to find an accurate and reliable brain Effective Connectome
(EC). Although current EC discovery methods have contributed to our understanding of brain …

Time-series analysis is useful for many applications in the field of epilepsy. From data
sources including seizure recording devices, patient seizure diaries, functional MRI and …