Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Denti, Francesco"'
Autor:
D'Angelo, Laura, Denti, Francesco
The use of hierarchical mixture priors with shared atoms has recently flourished in the Bayesian literature for partially exchangeable data. Leveraging on nested levels of mixtures, these models allow the estimation of a two-layered data partition: a
Externí odkaz:
http://arxiv.org/abs/2406.13310
Autor:
Benedetti, Luca, Boniardi, Eric, Chiani, Leonardo, Ghirri, Jacopo, Mastropietro, Marta, Cappozzo, Andrea, Denti, Francesco
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 instances of an unlabeled test set while allowing for the presence of previo
Externí odkaz:
http://arxiv.org/abs/2212.01865
The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two com
Externí odkaz:
http://arxiv.org/abs/2205.00930
Autor:
Varghese, Abhishek, Santos-Fernandez, Edgar, Denti, Francesco, Mira, Antonietta, Mengersen, Kerrie
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 Covid-19 Stringency Index, a measure describing a country's stringency of
Externí odkaz:
http://arxiv.org/abs/2203.04165
Autor:
Denti, Francesco, Azevedo, Ricardo, Lo, Chelsie, Wheeler, Damian, Gandhi, Sunil P., Guindani, Michele, Shahbaba, Babak
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. Most existing statistical methods solve this problem by partitioning t
Externí odkaz:
http://arxiv.org/abs/2106.08281
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 are essential. Numerous dimensionality reduction methods rely on the c
Externí odkaz:
http://arxiv.org/abs/2104.13832
Autor:
Denti, Francesco
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 for most dimensionality reduction techniques. In order to make these no
Externí odkaz:
http://arxiv.org/abs/2102.11425
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 particular, models for partially exchangeable data are needed for infer
Externí odkaz:
http://arxiv.org/abs/2008.07077
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 interest in identifying specific structures within the novelty, and co
Externí odkaz:
http://arxiv.org/abs/2006.09012
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 dimensional data is often challenging for statistical inference and dec
Externí odkaz:
http://arxiv.org/abs/2002.04148