Umberto Picchini

Umberto Picchini

Publications

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My Xi'an number is 4 (...just for fun).

    Working papers

  1. H. Häggström, P. Rodrigues, G. Oudoumanessah, F. Forbes and U. Picchini. Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings, arxiv:2403.07454.
  2. P. Jovanovski, A. Golightly and U. Picchini (2023). Towards data-conditional simulation for ABC inference in stochastic differential equations, arxiv:2310.10329.
  3. U. Picchini and M. Tamborrino (2022). Guided sequential ABC schemes for intractable Bayesian models, arXiv:2206.12235.
  4. S. Wiqvist, J. Frellsen and U. Picchini (2021). Sequential neural posterior and likelihood approximation, arxiv:2102.06522. [code]
  5. U. Picchini and R. Everitt (2019). Stratified sampling and bootstrapping for approximate Bayesian computation, arXiv:1905.07976. [code]
  6. S. Wiqvist, U. Picchini, J. Forman, K. Lindorff-Larsen and W. Boomsma (2018). Accelerating delayed-acceptance Markov chain Monte Carlo algorithms, arXiv:1806.05982. [code]. Christian P. Robert blogged about this paper.

    Peer-reviewed articles

    (contact me if you can't access a specific paper)
  7. K. Konstantinou, F. Ghorbanpour, U. Picchini, A. Loavenbruck, A. Särkkä (2023). Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality, Statistics in Medicine vol 42 issue 23 pp 4128-4146, also arXiv:2302.06374.
  8. S. Radev, M. Schmitt, V. Pratz, U. Picchini, U. Köthe, P. Bürkner (2023). JANA: jointly amortized neural approximation of complex Bayesian models. The 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023), vol 216, pp. 1695-1706.
  9. S. Persson, N. Welkenhuysen, S. Shashkova, S. Wiqvist, P. Reith, G. W. Schmidt, U. Picchini, M. Cvijovic (2022). Scalable and flexible inference framework for stochastic dynamic single-cell models, PLOS Computational Biology, 18(5):e1010082. [code]
  10. U. Picchini, U. Simola, J. Corander (2021). Sequentially guided MCMC proposals for synthetic likelihoods and correlated synthetic likelihoods. Bayesian Analysis, doi:10.1214/22-BA1305. [code]
  11. S. Wiqvist, A. Golightly, AT McLean, U. Picchini (2020). Efficient inference for stochastic differential mixed-effects models using correlated particle pseudo-marginal algorithms, Computational Statistics & Data Analysis, 157, 107151, also arXiv:1907.09851. [code]
  12. S. Wiqvist, P-A. Mattei, U. Picchini and J. Frellsen (2019). Partially Exchangeable Networks and architectures for learning summary statistics in Approximate Bayesian Computation. Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6798--6807. [code]. Christian P. Robert blogged about this paper.
  13. U. Picchini and J. Forman (2019). Bayesian inference for stochastic differential equation mixed effects models of a tumor xenography study, Journal of the Royal Statistical Society (Series C), 68(4), 887-913, also arXiv:1607.02633. [data and code].
  14. U. Picchini (2019). Likelihood-free stochastic approximation EM for inference in complex models, Communications in Statistics - Simulation and Computation, vol. 48(3), 861-881. [code].
  15. U. Picchini and A. Samson (2018). Coupling stochastic EM and Approximate Bayesian Computation for parameter inference in state-space models. Computational Statistics 33(1):179-212. [code].
  16. U. Picchini and R. Anderson (2017). Approximate maximum likelihood estimation using data-cloning ABC. Computational Statistics and Data Analysis, vol. 105, 166-183. A discussion based on an earlier version of this paper is at Christian P. Robert's blog.
  17. U. Picchini and J.L. Forman (2015). Accelerating inference for diffusions observed with measurement error and large sample sizes using Approximate Bayesian Computation. Journal of Statistical Computation and Simulation, 86(1), 195-213. A discussion based on an earlier version of this paper is at Christian P. Robert's blog.
  18. U. Picchini (2014). Inference for SDE models via Approximate Bayesian Computation. Journal of Computational and Graphical Statistics, 23(4), 1080-1100. [code].
  19. U. Picchini and S. Ditlevsen (2011). Practical estimation of high dimensional stochastic differential mixed-effects models. Computational Statistics & Data Analysis, 55(3), 1426-1444.
  20. U. Picchini, A. De Gaetano and S. Ditlevsen (2010). Stochastic differential mixed-effects models. Scandinavian Journal of Statistics, 37(1), 67-90. See also the corresponding Correction.
  21. U. Picchini, S. Ditlevsen and A. De Gaetano (2008). Maximum likelihood estimation of a time-inhomogeneous stochastic differential model of glucose dynamics. Mathematical Medicine and Biology, 25(2), 141-155.
  22. U. Picchini, S. Ditlevsen, A. De Gaetano and P. Lansky (2008). Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal. Neural Computation, 20(11), 2696-2714.
  23. P. Palumbo, U. Picchini, B. Beck, J. van Gelder, N. Delbar, A. De Gaetano (2008). A general approach to the apparent permeability index. Journal of Pharmacokinetics and Pharmacodynamics, 35(2), 235-248.
  24. U. Picchini, S. Ditlevsen and A. De Gaetano (2006). Modeling the euglycemic hyperinsulinemic clamp by stochastic differential equations. Journal of Mathematical Biology, 53(5), 771–796.
  25. A. Morelli, J.L. Teboul, S. M. Maggiore, A. Vieillard-Baron, M. Rocco, G. Conti, A. De Gaetano, U. Picchini, A. Orecchioni, I. Carbone, P. Pietropaoli, M. Westphal (2006). Effects Of Levosimendan On Right Ventricular Afterload In Patients With Acute Respiratory Distress Syndrome: A Pilot Study. Critical Care Medicine, 34(9):2287-2293.
  26. U. Picchini, A. De Gaetano, S. Panunzi, S. Ditlevsen and G. Mingrone (2005). A mathematical model of the euglycemic hyperinsulinemic clamp. Theoretical Biology and Medical Modelling, 3;2(1):44.
  27. A. Morelli, Z. Ricci, R. Bellomo, C. Ronco, M. Rocco, G. Conti, A. De Gaetano, U. Picchini, A. Orecchioni, M. Portieri, F. Coluzzi, P. Porzi, P. Serio, A. Bruno and P. Pietropaoli (2005). Prophylactic fenoldopam for renal protection in sepsis: a randomized, double blind, placebo-controlled pilot trial. Critical Care Medicine, 33(11):2451-2456.
  28. A. Morelli, L. Tritapepe, M. Rocco, G. Conti, A. Orecchioni, A. De Gaetano, U. Picchini, P. Pelaia, C. Reale and P. Pietropaoli (2005). Terlipressin versus Norepinephrine To Counteract Anesthesia-induced Hypotension in Patients Treated with Renin-Angiotensin System Inhibitors: Effects of Systemic and Regional Hemodynamics, Anesthesiology, 102(1):12-19.
  29. A. De Gaetano, G. Cortese, M.G. Pedersen, S. Panunzi, U. Picchini and A. Morelli (2004). Modeling serum creatinine in septic ICU patients, Cardiovascular Engineering: An International Journal, 4(2), 173-180.

    30. Research reports

  30. U. Picchini, S. Ditlevsen and A. De Gaetano (2005). System noise modelization in glucose/insulin dynamics. Technical Report R.630, IASI-CNR, Rome, Italy.
  31. U. Picchini, A. De Gaetano and S. Ditlevsen (2006). Parameter estimation in stochastic differential mixed-effects models. Research Report 06/12, Department of Biostatistics, University of Copenhagen.

    32. My PhD dissertation

  32. U. Picchini (2007). Stochastic Differential Models with Applications to Physiology. Department of Statistics, Probability and Applied Statistics, University of Rome "La Sapienza".