15 citations · 16 across the 7 of their papers we have counts for
8 papers
Smooth Isotropic Covariance Functions on Metric Graphs via Polyharmonic Resistance Distances
Tobia Filosi, Emilio Porcu, Claudio Agostinelli
Metric graphs are generalisations of linear networks and provide a natural framework for the definition of continuously-indexed Gaussian processes. We define a new class of distanc…
Scalable model selection for count time series with structural breaks: application to solid-organ transplantation during and after COVID-19 in the USA and Italy
Tobia Filosi, Emiliano Ceccarelli, Emilio Porcu +7
Weekly healthcare activity data are typically non-negative counts with temporal dependence and occasional system-wide disruptions, settings in which Gaussian time-series models may…
Vector-Valued Gaussian Processes and their Kernels on a Class of Metric Graphs
Tobia Filosi, Emilio Porcu, Xavier Emery +2
Despite the increasing importance of stochastic processes on linear networks and graphs, current literature on multivariate (vector-valued) Gaussian random fields on metric graphs…
Unified Native Spaces in Kernel Methods
Xavier Emery, Emilio Porcu, Moreno Bevilacqua
There exists a plethora of parametric models for positive definite kernels, and their use is ubiquitous in disciplines as diverse as statistics, machine learning, numerical analysi…
Matern and Generalized Wendland correlation models that parameterize hole effect, smoothness, and support
Xavier Emery, Moreno Bevilacqua, Emilio Porcu
A huge literature in statistics and machine learning is devoted to parametric families of correlation functions, where the correlation parameters are used to understand the propert…
Computationally Efficient Algorithms for Simulating Isotropic Gaussian Random Fields on Graphs with Euclidean Edges
Alfredo Alegría, Xavier Emery, Tobia Filosi +1
This work addresses the problem of simulating Gaussian random fields that are continuously indexed over a class of metric graphs, termed graphs with Euclidean edges, being more gen…
Temporally-Evolving Generalised Networks and their Reproducing Kernels
Tobia Filosi, Claudio Agostinelli, Emilio Porcu
This paper considers generalised network, intended as networks where (a) the edges connecting the nodes are nonlinear, and (b) stochastic processes are continuously indexed over bo…
Buhmann covariance functions, their compact supports, and their smoothness
E. Porcu, P. Zastavyi, M. Bevilacqua
We consider the Buhmann class of compactly supported radial basis functions, whih includes a wealth of special cases that have been studied in both numerical analysis and spatial s…