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Massimiliano Tamborrino
Massimiliano Tamborrino
Associate Professor, Department of Statistics, University of Warwick
Email verificata su warwick.ac.uk - Home page
Titolo
Citata da
Citata da
Anno
Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies
MJ Keeling, EM Hill, EE Gorsich, B Penman, G Guyver-Fletcher, A Holmes, ...
PLoS computational biology 17 (1), e1008619, 2021
1162021
Count Data Analysis in Randomised Clinical Trials
JC Jakobsen, M Tamborrino, P Winkel, N Haase, A Perner, J Wetterslev, ...
Journal of Biometrics & Biostatistics 6 (227), 2015
442015
A review of the methods for neuronal response latency estimation
M Levakova, M Tamborrino, S Ditlevsen, P Lansky
Biosystems 136, 23--34, 2015
402015
Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs
E Buckwar, M Tamborrino, I Tubikanec
Statistics and Computing 30, 627-648, 2020
332020
First passage times of two-dimensional correlated processes: Analytical results for the Wiener process and a numerical method for diffusion processes
L Sacerdote, M Tamborrino, C Zucca
Journal of Computational and Applied Mathematics 296, 275-292, 2016
33*2016
A splitting method for SDEs with locally Lipschitz drift: Illustration on the FitzHugh-Nagumo model
E Buckwar, A Samson, M Tamborrino, I Tubikanec
Applied Numerical Mathematics 179, 191-220, 2022
262022
Detecting dependencies between spike trains of pairs of neurons through copulas
L Sacerdote, M Tamborrino, C Zucca
Brain research 1434, 243-256, 2012
242012
Weak convergence of marked point processes generated by crossings of multivariate jump processes. Applications to neural network modeling
M Tamborrino, L Sacerdote, M Jacobsen
Physica D: Nonlinear Phenomena 288, 45-52, 2014
182014
Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion
I Tubikanec, M Tamborrino, P Lansky, E Buckwar
Journal of Computational and Applied Mathematics 406, 113951, 2022
17*2022
The Jacobi diffusion process as a neuronal model
G D’Onofrio, M Tamborrino, P Lansky
Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (10), 2018
162018
Parameter inference from hitting times for perturbed Brownian motion
M Tamborrino, S Ditlevsen, P Lansky
Lifetime Data Analysis 21, 331--352, 2015
132015
Identification of noisy response latency
M Tamborrino, S Ditlevsen, P Lansky
Physical Review E 86 (2), 021128, 2012
132012
Parametric inference of neuronal response latency in presence of a background signal
M Tamborrino, S Ditlevsen, P Lansky
BioSystems 112 (3), 249-257, 2013
102013
Shot noise, weak convergence and diffusion approximations
M Tamborrino, P Lansky
Physica D: Nonlinear Phenomena 418, 132845, 2021
92021
Stochastic parareal: an application of probabilistic methods to time-parallelization
K Pentland, M Tamborrino, D Samaddar, LC Appel
SIAM Journal on Scientific Computing, S82-S102, 2022
72022
Accuracy of rate coding: When shorter time window and higher spontaneous activity help
M Levakova, M Tamborrino, L Kostal, P Lansky
Physical Review E 95 (2), 022310, 2017
72017
Inhibition enhances the coherence in the Jacobi neuronal model
G D’Onofrio, P Lansky, M Tamborrino
Chaos, Solitons & Fractals 128, 108-113, 2019
62019
Presynaptic spontaneous activity enhances the accuracy of latency coding
M Levakova, M Tamborrino, L Kostal, P Lansky
Neural Computation 28 (10), 2162-2180, 2016
62016
GParareal: a time-parallel ODE solver using Gaussian process emulation
K Pentland, M Tamborrino, TJ Sullivan, J Buchanan, LC Appel
Statistics and Computing 33 (1), 23, 2023
42023
Guided sequential ABC schemes for intractable Bayesian models
U Picchini, M Tamborrino
arXiv preprint arXiv:2206.12235, 2022
42022
Il sistema al momento non pu eseguire l'operazione. Riprova pi tardi.
Articoli 1–20