3 citations · 3 across the 9 of their papers we have counts for
7 papers · 1 filter
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…