I. Tubikanec

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A splitting method for SDEs with locally Lipschitz drift: Illustration on the FitzHugh-Nagumo model

E. Buckwar, M. Tamborrino, Adeline M. M. Samson, 2021, Applied Numerical Mathematics.

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

E. Buckwar, M. Tamborrino, P. Lansky, 2021, J. Comput. Appl. Math..

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

E. Buckwar, M. Tamborrino, I. Tubikanec, 2019, Statistics and Computing.

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

Evelyn Buckwar, Massimiliano Tamborrino, Irene Tubikanec, 2019, Statistics and Computing.

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

Evelyn Buckwar, Irene Tubikanec, Massimiliano Tamborrino, 2021, ArXiv.

Diffusion approximation of multi-class Hawkes processes: Theoretical and numerical analysis

A. Melnykova, Julien Chevallier, I. Tubikanec, 2021, Advances in Applied Probability.

Theoretical analysis and simulation methods for Hawkes processes and their diffusion approximation pdf

Irene Tubikanec, Julien Chevallier, Anna Melnykova, 2020, ArXiv.

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

M. Tamborrino, I. Tubikanec, Susanne Ditlevsen, 2023, ArXiv.

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

M. Tamborrino, I. Tubikanec, Adeline Samson, 2024, 2405.17972.