Qualitative properties of numerical methods for the inhomogeneous geometric Brownian motion. - Irene Tubikanec; Massimiliano Tamborrino; Petr Lánský; Evelyn Buckwar (CoRR, 2020)

Theoretical analysis and simulation methods for Hawkes processes and their diffusion approximation. - Julien Chevallier; Anna Melnykova; Irene Tubikanec (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)

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)

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length. - Katerina Hlavácková-Schindler; Anna Melnykova; Irene Tubikanec (J. Mach. Learn. Res., 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)