| Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies MJ Keeling, EM Hill, EE Gorsich, B Penman, G Guyver-Fletcher, A Holmes, ... PLoS computational biology 17 (1), e1008619, 2021 | 128 | 2021 |
| Count Data Analysis in Randomised Clinical Trials JC Jakobsen, M Tamborrino, P Winkel, N Haase, A Perner, J Wetterslev, ... Journal of Biometrics & Biostatistics 6 (227), 2015 | 48 | 2015 |
| A review of the methods for neuronal response latency estimation M Levakova, M Tamborrino, S Ditlevsen, P Lansky Biosystems 136, 23--34, 2015 | 44 | 2015 |
| First passage times of two-dimensional correlated processes: Analytical results for the Wiener process and a numerical method for diffusion processes L Sacerdote, M Tamborrino, C Zucca Journal of Computational and Applied Mathematics 296, 275-292, 2016 | 38* | 2016 |
| A splitting method for SDEs with locally Lipschitz drift: Illustration on the FitzHugh-Nagumo model E Buckwar, A Samson, M Tamborrino, I Tubikanec Applied Numerical Mathematics 179, 191-220, 2022 | 35 | 2022 |
| Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs E Buckwar, M Tamborrino, I Tubikanec Statistics and Computing 30 (3), 627-648, 2020 | 33 | 2020 |
| Detecting dependencies between spike trains of pairs of neurons through copulas L Sacerdote, M Tamborrino, C Zucca Brain research 1434, 243-256, 2012 | 25 | 2012 |
| Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion I Tubikanec, M Tamborrino, P Lansky, E Buckwar Journal of Computational and Applied Mathematics 406, 113951, 2022 | 20* | 2022 |
| The Jacobi diffusion process as a neuronal model G D’Onofrio, M Tamborrino, P Lansky Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (10), 2018 | 16 | 2018 |
| Weak convergence of marked point processes generated by crossings of multivariate jump processes. Applications to neural network modeling M Tamborrino, L Sacerdote, M Jacobsen Physica D: Nonlinear Phenomena 288, 45-52, 2014 | 16 | 2014 |
| Parameter inference from hitting times for perturbed Brownian motion M Tamborrino, S Ditlevsen, P Lansky Lifetime Data Analysis 21, 331--352, 2015 | 13 | 2015 |
| Identification of noisy response latency M Tamborrino, S Ditlevsen, P Lansky Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 86 (2 …, 2012 | 13 | 2012 |
| Stochastic parareal: An application of probabilistic methods to time-parallelization K Pentland, M Tamborrino, D Samaddar, LC Appel SIAM Journal on Scientific Computing 45 (3), S82-S102, 2022 | 10 | 2022 |
| Shot noise, weak convergence and diffusion approximations M Tamborrino, P Lansky Physica D: Nonlinear Phenomena 418, 132845, 2021 | 10 | 2021 |
| Parametric inference of neuronal response latency in presence of a background signal M Tamborrino, S Ditlevsen, P Lansky BioSystems 112 (3), 249-257, 2013 | 10 | 2013 |
| GParareal: a time-parallel ODE solver using Gaussian process emulation K Pentland, M Tamborrino, TJ Sullivan, J Buchanan, LC Appel Statistics and Computing 33 (1), 23, 2023 | 8 | 2023 |
| Inhibition enhances the coherence in the Jacobi neuronal model G D’Onofrio, P Lansky, M Tamborrino Chaos, Solitons & Fractals 128, 108-113, 2019 | 7 | 2019 |
| Accuracy of rate coding: When shorter time window and higher spontaneous activity help M Levakova, M Tamborrino, L Kostal, P Lansky Physical Review E 95 (2), 022310, 2017 | 7 | 2017 |
| Presynaptic spontaneous activity enhances the accuracy of latency coding M Levakova, M Tamborrino, L Kostal, P Lansky Neural Computation 28 (10), 2162-2180, 2016 | 6 | 2016 |
| Guided sequential ABC schemes for intractable Bayesian models U Picchini, M Tamborrino arXiv preprint arXiv:2206.12235, 2022 | 5 | 2022 |
Irene TubikanecJohannes Kepler University Linz在 jku.at 的电子邮件经过验证
