5 citations · 10 across the 9 of their papers we have counts for
10 papers
Stochastic compliance/evasion dynamics in tax models: a piecewise deterministic Markov process approach
Jonas Mayr, Amira Meddah, Irene Tubikanec
This paper introduces a novel stochastic framework for modelling tax evasion dynamics by extending the deterministic model of Bertotti and Modanese (2018) through the use of Piecew…
Splitting methods for stochastic Hodgkin-Huxley type systems and a localized fundamental mean-square convergence theorem
Pierre Étoré, Anna Melnykova, Irene Tubikanec
Existing fundamental theorems for mean-square convergence of numerical methods for stochastic differential equations (SDEs) require globally or one-sided Lipschitz continuous coeff…
Approximate Bayesian computation for stochastic hybrid systems with empirically observed ergodic behaviour
Sascha Desmettre, Agnes Mallinger, Amira Meddah +1
Piecewise diffusion Markov processes (PDifMPs) form a versatile class of stochastic hybrid systems that combine continuous diffusion processes with discrete event-driven dynamics,…
Inference for the stochastic FitzHugh-Nagumo model from real action potential data via approximate Bayesian computation
Adeline Samson, Massimiliano Tamborrino, Irene Tubikanec
The stochastic FitzHugh-Nagumo (FHN) model is a two-dimensional nonlinear stochastic differential equation with additive degenerate noise, whose first component, the only one obser…
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length
Katerina Hlavackova-Schindler, Anna Melnykova, Irene Tubikanec
Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used to model various real-life phenomena: earthquakes, operations on stock markets, neuronal activity, virus…
Network inference via approximate Bayesian computation. Illustration on a stochastic multi-population neural mass model
Susanne Ditlevsen, Massimiliano Tamborrino, Irene Tubikanec
In this article, we propose an adapted sequential Monte Carlo approximate Bayesian computation (SMC-ABC) algorithm for network inference in coupled stochastic differential equation…
A splitting method for SDEs with locally Lipschitz drift: Illustration on the FitzHugh-Nagumo model
Evelyn Buckwar, Adeline Samson, Massimiliano Tamborrino +1
In this article, we construct and analyse an explicit numerical splitting method for a class of semi-linear stochastic differential equations (SDEs) with additive noise, where the…
Theoretical analysis and simulation methods for Hawkes processes and their diffusion approximation
Julien Chevallier, Anna Melnykova, Irene Tubikanec
Oscillatory systems of interacting Hawkes processes with Erlang memory kernels were introduced in Ditlevsen (2017). They are piecewise deterministic Markov processes (PDMP) and can…
Qualitative properties of numerical methods for the inhomogeneous geometric Brownian motion
Irene Tubikanec, Massimiliano Tamborrino, Petr Lansky +1
We provide a comparative analysis of qualitative features of different numerical methods for the inhomogeneous geometric Brownian motion (IGBM). The conditional and asymptotic mean…
Spectral Density-Based and Measure-Preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs
Evelyn Buckwar, Massimiliano Tamborrino, Irene Tubikanec
Approximate Bayesian Computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differe…