3 citations · 3 across the 6 of their papers we have counts for
6 papers
A Bayesian Approach for Inference on Mixed Graphical Models
Mauro Florez, Anna Gottard, Carrie McAdams +2
Mixed data refers to a type of data in which variables can be of multiple types, such as continuous, discrete, or categorical. This data is routinely collected in various fields, i…
BISON: Bi-clustering of spatial omics data with feature selection
Bencong Zhu, Alberto Cassese, Marina Vannucci +2
The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profilin…
Clustering Computer Mouse Tracking Data with Informed Hierarchical Shrinkage Partition Priors
Ziyi Song, Weining Shen, Marina Vannucci +4
Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive process…
Bayesian Bivariate Conway-Maxwell-Poisson Regression Model for Correlated Count Data in Sports
Mauro Florez, Michele Guindani, Marina Vannucci
Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data a…
Bayesian temporal biclustering with applications to multi-subject neuroscience studies
Federica Zoe Ricci, Erik B. Sudderth, Jaylen Lee +3
We consider the problem of analyzing multivariate time series collected on multiple subjects, with the goal of identifying groups of subjects exhibiting similar trends in their rec…
Bayesian Varying-Effects Vector Autoregressive Models for Inference of Brain Connectivity Networks and Covariate Effects in Pediatric Traumatic Brain Injury
Yangfan Ren, Nathan Osborne, Christine B. Peterson +3
In this paper, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extensio…