This paper presents a Bayesian reformulation of covariate-assisted principal regression for
covariance matrix outcomes to identify low-dimensional components in the covariance …

Abstract Recently, the “human-oriented” concept of security development has become a
consensus among all countries. This depends mainly on intelligent surveillance systems that …

Identifying individual mechanisms involved in complex diseases, such as cancer, is
essential for precision medicine. Their characterization is particularly challenging due to the …

Exploring and detecting community structures hold significant importance in genetics, social
sciences, neuroscience, and finance. Especially in graphical models, community detection …

Heterogeneous and context-dependent systems are common in real-world processes, such
as those in biology, medicine, finance, and the social sciences. However, learning accurate …

Assessing how brain functional connectivity networks vary across individuals promises to
uncover important scientific questions such as patterns of healthy brain aging through the …

Probabilistic graphical models are powerful and widely used tools to quantify, visualize and
interpret dependencies in complex biological systems such as highthroughput genomics …

In contemporary medical research, patient heterogeneity plays a pivotal role in
comprehending intricate diseases such as Alzheimer's disease and various forms of cancer …

Networks have the critical ability to represent the complex interconnectedness of social
relationships, biological processes, and the spread of diseases and information. Exponential …

Bayesian statistics is an approach to analyzing data based on the Bayes theorem: model
parameters are assigned distributions to incorporate available prior knowledge and capture …