3 citations · 10 across the 9 of their papers we have counts for
9 papers
Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets
Luca Benedetti, Eric Boniardi, Leonardo Chiani +4
After being trained on a fully-labeled training set, where the observations are grouped into a certain number of known classes, novelty detection methods aim to classify the instan…
Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies
Francesco Denti, Stefano Peluso, Michele Guindani +1
The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly m…
On the intrinsic dimensionality of Covid-19 data: a global perspective
Abhishek Varghese, Edgar Santos-Fernandez, Francesco Denti +2
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covi…
A Horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging
Francesco Denti, Ricardo Azevedo, Chelsie Lo +4
In this paper, we focus on identifying differentially activated brain regions using a light sheet fluorescence microscopy - a recently developed technique for whole-brain imaging.…
Distributional Results for Model-Based Intrinsic Dimension Estimators
Francesco Denti, Diego Doimo, Alessandro Laio +1
Modern datasets are characterized by a large number of features that may conceal complex dependency structures. To deal with this type of data, dimensionality reduction techniques…
intRinsic: an R Package for Model-Based Estimation of the Intrinsic Dimension of a Dataset
Francesco Denti
This article illustrates intRinsic, an R package that implements novel state-of-the-art likelihood-based estimators of the intrinsic dimension of a dataset, an essential quantity f…
A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data
Francesco Denti, Federico Camerlenghi, Michele Guindani +1
The use of high-dimensional data for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In p…
A Two-Stage Bayesian Semiparametric Model for Novelty Detection with Robust Prior Information
Francesco Denti, Andrea Cappozzo, Francesca Greselin
Novelty detection methods aim at partitioning the test units into already observed and previously unseen patterns. However, two significant issues arise: there may be considerable…
The role of intrinsic dimension in high-resolution player tracking data -- Insights in basketball
Edgar Santos-Fernandez, Francesco Denti, Kerrie Mengersen +1
A new range of statistical analysis has emerged in sports after the introduction of the high-resolution player tracking technology, specifically in basketball. However, this high d…