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Publications

Get updates on new publications through my Google ScholarLink opens in a new window and Research GateLink opens in a new window.

Submitted

26. S. Ditlevsen, M. Tamborrino, I. Tubikanec (2023) Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation.Link opens in a new window Preprint at arXiv:2306.15787.

25. A. Ahari, L. Alili, M. Tamborrino (2022) Boundary crossing problems and functional transformations for Ornstein-Uhlenbeck processesLink opens in a new window. Preprint at arXiv:2210.01658.

24. U. Picchini, M. Tamborrino (2022) Guided sequential ABC schemes for intractable Bayesian ModelsLink opens in a new window. Preprint at arXiv:2206.12235.

    Publications

    23. K. Pentland, M. Tamborrino, T. Sullivan. Error bound analysis of the stochastic parareal algorithmLink opens in a new window. SIAM J. Scient. Comput. 45(5), 2023.

    22. K. Pentland, M. Tamborrino, T.J. Sullivan, J. Buchanan, L. Appel. GParareal: A time-parallel ODE solver using Gaussian process emulationLink opens in a new window, Stats. Comput. 33, 2023.

    21. K. Pentland, M. Tamborrino, D. Samaddar, L. Appel. Stochastic parareal: An application of probabilistic methods to time-parallelisationLink opens in a new window. SIAM J. Scient. Comput. 45 (3), 2023.

    20. E. Buckwar, A. Samson, M. Tamborrino, I. Tubikanec. Splitting methods for SDEs with locally Lipschitz drift. An illustration on the FitzHugh-Nagumo modelLink opens in a new window. Appl. Num. Math., 179, 191-220, 2022.

    19. I. Tubikanec, M. Tamborrino, P. Lansky, E. Buckwar. Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motionLink opens in a new window, J. Comput. Appl. Math., 406, 113951, 2022.

    18. M. Tamborrino, P. Lansky Shot noise, weak convergence and diffusion approximations. Link opens in a new windowPhysica D, 148, 132845, 2021. The R package for the exact simulation of shot noise processes, and non-Gaussian OU-processes (OU-Poisson, OU-Gamma, OU-Inverse Gaussian) is available on GitHubLink opens in a new window.

    17. M.J. Keeling, E. Hill, E. Gorsich, B. Penman, G. Guyver-Fletcher, A. Holmes, T. Leng, H. McKimm, M. Tamborrino, L. Dyson, M.Tildesley Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategiesLink opens in a new window. Plos Comput Biol. 171(1), e10086, 2021

      16. E. Buckwar, M. Tamborrino, I. Tubikanec. Spectral Density-Based and Measure-Preserving ABC for partially observed diffusion processes. A Demonstration on Hamiltonian SDEs,Link opens in a new window Stat. Comput., 30, 627-648, 2020. [Code]Link opens in a new window.
      I spoke about this work at the Bernoulli-IMS One World Symposium 2020Link opens in a new window. My pre-recorded 10minutes talk is available hereLink opens in a new window.

      15. G. D'Onofrio, P. Lansky, M. Tamborrino. Inhibition enhances the coherence in the Jacobi neuronal modelLink opens in a new window, Chaos Sol. Fract., 128, 108-113, 2019.

      14. G. D'Onofrio, M. Tamborrino, P. Lansky. The Jacobi diffusion process as a neuronal modelLink opens in a new window, Chaos 28, 103119, 1-10, 2018.

      13. M. Levakova, M. Tamborrino, L. Kostal and P. Lansky. Accuracy of rate coding: When shorter time window and higher spontaneous activity helpLink opens in a new window. Phys. Rev. E, 95(2): 022310, 2017.

      12. M. Tamborrino, S. Ditlevsen, B. Markussen and S. Kyllingsb├Žk. Gaussian counter models for Visual Identification of Briefly Presented, Mutually Confusable Single Stimuli in Pure Accuracy TasksLink opens in a new window. Math. Psychol., 79, 85-103, 2017.

      11.M. Levakova, M. Tamborrino, L. Kostal and P. Lansky. Presynaptic spontaneous activity enhances the accuracy of latency coding.Link opens in a new window Neural Comput., 28, 2162-2180, 2016.

      10. L. Sacerdote, M. Tamborrino and C. Zucca. First passage times of two-dimensional correlated processes: analytical results for the Wiener process and a numerical method for diffusion processesLink opens in a new window. J. Comput. Appl. Math., 296, 275-292, 2016.

      9 M. Tamborrino. Approximation of the first passage time density of a Brownian motion to an exponentially decaying threshold by two-piecewise linear threshold. Application to neuronal spiking activityLink opens in a new window. Math. Biosci. Eng., 13 (3), 613-629, 2016.

      8. J. C. Jacobsen, M. Tamborrino, P. Winkel, N. Haase, A. Perner, J. Wetterslev and C. Gluud. Count Data Analysis in Randomised Clinical TrialsLink opens in a new window. J. Biom Biostat. 6, 1, 2015.

      7. M. Levakova, M. Tamborrino, S. Ditlevsen and P. Lansky. A review of the methods for neuronal response latency estimationLink opens in a new window. BioSystem, 136, 23-34, 2015

      6. M. Tamborrino, S. Ditlevsen and P. Lansky. Parametric inference from hitting times for perturbed Brownian motionLink opens in a new window. Lifetime Data Anal. 21 (3): 331-352, 2015.

      5. Tamborrino, L. Sacerdote and M. Jacobsen. Weak convergence of marked point processes generated by crossings of multivariate jump processes. Application to neural network modelingLink opens in a new window. Physica D, 288: 45-52, 2014.

      4. M. Tamborrino, S. Ditlevsen and P. Lansky. Parametric inference of neuronal response latency in presence of a background signalLink opens in a new window. {\em BioSystems}, 112: 249--257, 2013.

      3. M. Tamborrino, S. Ditlevsen and P. Lansky. Identification of noisy response latencyLink opens in a new window. Phys. Rev. E, 86, 021128, 2012.

      2. L. Sacerdote, M. Tamborrino and C. Zucca. Detecting dependencies between spike trains of pairs of neurons through copulasLink opens in a new window. Brain Res., 1434: 243--256, 2012.

      1. L. Sacerdote and M. Tamborrino. Leaky Integrate and Fire models coupled through copulas: association properties of the Interspike IntervalsLink opens in a new window. J. Physiol., 53 (6): 396-406, 2010.