3 citations · 3 across the 9 of their papers we have counts for
10 papers
Bayesian Multi-Group Functional Factor Models with Parameter-Expanded Cumulative Shrinkage Priors
Xuanye Dai, Anna Gottard, Michele Guindani +1
Functional data consist of trajectories observed over a continuous domain, such as time, space, or wavelength. Here we consider curves observed on different groups of subjects and…
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…
Bayesian Controlled FDR Variable Selection via Parameter-Expanded Latent Knockoffs
Lorenzo Focardi-Olmi, Anna Gottard, Michele Guindani +1
In many research fields, researchers aim to identify significant associations between a set of explanatory variables and a response while controlling the FDR. The Knockoff filter h…
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…
Local Level Dynamic Random Partition Models for Changepoint Detection
Alice Giampino, Bernardo Nipoti, Marina Vannucci +1
Motivated by an increasing demand for models that can effectively describe features of complex multivariate time series, e.g. from sensor data in biomechanics, motion analysis, and…
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…
Discrete Autoregressive Switching Processes with Cumulative Shrinkage Priors for Graphical Modeling of Time Series Data
Beniamino Hadj-Amar, Aaron M. Bornstein, Michele Guindani +1
We propose a flexible Bayesian approach for sparse Gaussian graphical modeling of multivariate time series. We account for temporal correlation in the data by assuming that observa…
Flexible aggregation of compositional predictors with shared effects for microbiome association analysis
Satabdi Saha, Liangliang Zhang, Michele Guindani +2
Ongoing advancements in microbiome profiling have provided unprecedented insights into the molecular dynamics of microbial communities, sparking a surge of interest in uncovering t…