Parametric inference of neuronal response latency in presence of a background signal. - Massimiliano Tamborrino; Susanne Ditlevsen; Petr Lánský (Biosyst., 2013)

A review of the methods for neuronal response latency estimation. - Marie Levakova; Massimiliano Tamborrino; Susanne Ditlevsen; Petr Lánský (Biosyst., 2015)

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)

Presynaptic Spontaneous Activity Enhances the Accuracy of Latency Coding. - Marie Levakova; Massimiliano Tamborrino; Lubomir Kostal; Petr Lánský (Neural Comput., 2016)

Qualitative properties of numerical methods for the inhomogeneous geometric Brownian motion. - Irene Tubikanec; Massimiliano Tamborrino; Petr Lánský; Evelyn Buckwar (CoRR, 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)

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)

Stochastic parareal: an application of probabilistic methods to time-parallelisation. - Kamran Pentland; Massimiliano Tamborrino; D. Samaddar; Lynton C. Appel (CoRR, 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)

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)

Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation. - Susanne Ditlevsen; Massimiliano Tamborrino; Irene Tubikanec (CoRR, 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)

Stochastic Parareal: An Application of Probabilistic Methods to Time-Parallelization. - Kamran Pentland; Massimiliano Tamborrino; Debasmita Samaddar; Lynton C. Appel (SIAM J. Sci. Comput., 2023)

Error Bound Analysis of the Stochastic Parareal Algorithm. - Kamran Pentland; Massimiliano Tamborrino; Timothy John Sullivan (SIAM J. Sci. Comput., 2023)

RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks. - Guglielmo Gattiglio; Lyudmila Grigoryeva; Massimiliano Tamborrino (NeurIPS, 2024)

Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers. - Guglielmo Gattiglio; Lyudmila Grigoryeva; Massimiliano Tamborrino (CoRR, 2024)

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)

Boundary crossing problems and functional transformations for Ornstein-Uhlenbeck processes. - Aria Ahari; Larbi Alili; Massimiliano Tamborrino (J. Appl. Probab., 2025)