Massimiliano Tamborrino

According to our database, Massimiliano Tamborrino authored at least 16 papers between 2013 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Inference for the stochastic FitzHugh-Nagumo model from real action potential data via approximate Bayesian computation.

Adeline Samson, Massimiliano Tamborrino, Irene Tubikanec

Comput. Stat. Data Anal., 2025

2024

Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers.

Guglielmo Gattiglio, Lyudmila Grigoryeva, Massimiliano Tamborrino

CoRR, 2024

2023

Error Bound Analysis of the Stochastic Parareal Algorithm.

Kamran Pentland, Massimiliano Tamborrino, Timothy John Sullivan

SIAM J. Sci. Comput., October, 2023

Stochastic Parareal: An Application of Probabilistic Methods to Time-Parallelization.

Kamran Pentland, Massimiliano Tamborrino, Debasmita Samaddar, Lynton C. Appel

SIAM J. Sci. Comput., June, 2023

GParareal: a time-parallel ODE solver using Gaussian process emulation.

Kamran Pentland, Massimiliano Tamborrino, Timothy John Sullivan, James Buchanan, Lynton C. Appel

Stat. Comput., 2023

Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation.

Susanne Ditlevsen, Massimiliano Tamborrino, Irene Tubikanec

CoRR, 2023

2022

Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion.

Irene Tubikanec, Massimiliano Tamborrino, Petr Lánský, Evelyn Buckwar

J. Comput. Appl. Math., 2022

2021

Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies.

Matt J. Keeling, Edward M. Hill, Erin E. Gorsich, Bridget S. Penman, Glen Guyver-Fletcher, Alex Holmes, Trystan Leng, Hector McKimm, Massimiliano Tamborrino, Louise Dyson, Michael J. Tildesley

PLoS Comput. Biol., 2021

Stochastic parareal: an application of probabilistic methods to time-parallelisation.

Kamran Pentland, Massimiliano Tamborrino, D. Samaddar, Lynton C. Appel

CoRR, 2021

Splitting methods for SDEs with locally Lipschitz drift. An illustration on the FitzHugh-Nagumo model.

Evelyn Buckwar, Adeline Samson, Massimiliano Tamborrino, Irene Tubikanec

CoRR, 2021

2020

Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs.

Evelyn Buckwar, Massimiliano Tamborrino, Irene Tubikanec

Stat. Comput., 2020

Qualitative properties of numerical methods for the inhomogeneous geometric Brownian motion.

Irene Tubikanec, Massimiliano Tamborrino, Petr Lánský, Evelyn Buckwar

CoRR, 2020

2016

Presynaptic Spontaneous Activity Enhances the Accuracy of Latency Coding.

Marie Levakova, Massimiliano Tamborrino, Lubomir Kostal, Petr Lánský

Neural Comput., 2016

First passage times of two-dimensional correlated processes: Analytical results for the Wiener process and a numerical method for diffusion processes.

Laura Sacerdote, Massimiliano Tamborrino, Cristina Zucca

J. Comput. Appl. Math., 2016

2015

A review of the methods for neuronal response latency estimation.

Marie Levakova, Massimiliano Tamborrino, Susanne Ditlevsen, Petr Lánský

Biosyst., 2015

2013

Parametric inference of neuronal response latency in presence of a background signal.

Massimiliano Tamborrino, Susanne Ditlevsen, Petr Lánský

Biosyst., 2013