2 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
Compatibility of Space-Time Kernels with Full, Dynamical, or Compact Support
Tarik Faouzi, Reinhard Furrer, Emilio Porcu
We deal with the comparison of space-time covariance kernels having, either, full, spatially dynamical, or space-time compact support. Such a comparison is based on compatibility o…
The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning
Emilio Porcu, Moreno Bevilacqua, Robert Schaback +1
The Matérn model has been a cornerstone of spatial statistics for more than half a century. More recently, the Matérn model has been central to disciplines as diverse as numerical…
Hybrid Parametric Classes of Isotropic Covariance Functions for Spatial Random Fields
Alfredo Alegría, Fabián Ramírez, Emilio Porcu
Covariance functions are the core of spatial statistics, stochastic processes, machine learning as well as many other theoretical and applied disciplines. The properties of the cov…
Nonseparable Space-Time Stationary Covariance Functions on Networks cross Time
Emilio Porcu, Philip A. White, Marc G. Genton
The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain…