37 citations · 146 across the 15 of their papers we have counts for
17 papers
Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC Samplers
Alessandro Viani, Adam M Johansen, Alberto Sorrentino
In Bayesian inverse problems, one aims at characterizing the posterior distribution of a set of unknowns, given indirect measurements. For non-linear/non-Gaussian problems, analyti…
The role of spectral complexity in connectivity estimation
Elisabetta Vallarino, Michele Piana, Alberto Sorrentino +1
The study of functional connectivity from magnetoecenphalographic (MEG) data consists in quantifying the statistical dependencies among time series describing the activity of diffe…
Where Bayes tweaks Gauss: Conditionally Gaussian priors for stable multi-dipole estimation
Alessandro Viani, Gianvittorio Luria, Harald Bornfleth +1
We present a very simple yet powerful generalization of a previously described model and algorithm for estimation of multiple dipoles from magneto/electro-encephalographic data. Sp…
On the two-step estimation of the cross--power spectrum for dynamical inverse problems
Elisabetta Vallarino, Sara Sommariva, Michele Piana +1
We consider the problem of reconstructing the cross--power spectrum of an unobservable multivariate stochatic process from indirect measurements of a second multivariate stochastic…
Compressed sensing and Sequential Monte Carlo for solar hard X-ray imaging
Anna Maria Massone, Federica Sciacchitano, Michele Piana +1
We describe two inversion methods for the reconstruction of hard X-ray solar images. The methods are tested against experimental visibilities recorded by the Reuven Ramaty High Ene…
Bayesian Multi--Dipole Modeling in the Frequency Domain
Gianvittorio Luria, Dunja Duran, Elisa Visani +6
Background: Magneto- and Electro-encephalography record the electromagnetic field generated by neural currents with high temporal frequency and good spatial resolution, and are the…
Sparse Bayesian Imaging of Solar Flares
Federica Sciacchitano, Silvio Lugaro, Alberto Sorrentino
We consider imaging of solar flares from NASA RHESSI data as a parametric imaging problem, where flares are represented as a finite collection of geometric shapes. We set up a Baye…
Identification of multiple hard X-ray sources in solar flares: A Bayesian analysis of the February 20 2002 event
Federica Sciacchitano, Alberto Sorrentino, A Gordon Emslie +2
The hard X-ray emission in a solar flare is typically characterized by a number of discrete sources, each with its own spectral, temporal, and spatial variability. Establishing the…
A comparative study of the robustness of frequency--domain connectivity measures to finite data length
Sara Sommariva, Alberto Sorrentino, Michele Piana +2
In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time--series. We…
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings
Filippo Rossi, Gianvittorio Luria, Sara Sommariva +1
We deal with estimation of multiple dipoles from combined MEG and EEG time--series. We use a sequential Monte Carlo algorithm to characterize the posterior distribution of the numb…
Iterative algorithms for a non-linear inverse problem in atmospheric lidar
Giulia Denevi, Sara Garbarino, Alberto Sorrentino
We consider the inverse problem of retrieving aerosol extinction coefficients from Raman lidar measurements. In this problem the unknown and the data are related through the expone…
Inverse Modeling for MEG/EEG data
Alberto Sorrentino, Michele Piana
We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the…
Expectation Maximization and the retrieval of the atmospheric extinction coefficients by inversion of Raman lidar data
Sara Garbarino, Alberto Sorrentino, Anna Maria Massone +5
We consider the problem of retrieving the aerosol extinction coefficient from Raman lidar measurements. This is an ill--posed inverse problem that needs regularization, and we prop…
Bayesian smoothing of dipoles in Magneto-/Electro-encephalography
Valentina Vivaldi, Alberto Sorrentino
We describe a novel method for dynamic estimation of multi-dipole states from Magneto/Electro-encephalography (M/EEG) time series. The new approach builds on the recent development…
Sequential Monte Carlo samplers for semilinear inverse problems and application to magnetoencephalography
Sara Sommariva, Alberto Sorrentino
We discuss the use of a recent class of sequential Monte Carlo methods for solving inverse problems characterized by a semi-linear structure, i.e. where the data depend linearly on…
Bayesian Multi--Dipole Modeling of a Single Topography in MEG by Adaptive Sequential Monte Carlo Samplers
Alberto Sorrentino, Gianvittorio Luria, Riccardo Aramini
In the present paper, we develop a novel Bayesian approach to the problem of estimating neural currents in the brain from a fixed distribution of magnetic field (called \emph{topog…
Dynamic filtering of static dipoles in magnetoencephalography
Alberto Sorrentino, Adam M. Johansen, John A. D. Aston +2
We consider the problem of estimating neural activity from measurements of the magnetic fields recorded by magnetoencephalography. We exploit the temporal structure of the problem…