Publications
My Xi'an number is 4 (...just for fun).
- S. Wiqvist, J. Frellsen and U. Picchini (2021). Sequential neural posterior and likelihood approximation, arxiv:2102.06522. [code]
- U. Picchini and R. Everitt (2019). Stratified sampling and bootstrapping for approximate Bayesian computation, arXiv:1905.07976. [code]
- 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) - P. Jovanovski, A. Golightly and U. Picchini (2024). Towards data-conditional simulation for ABC inference in stochastic differential equations. Bayesian Analysis (advance publication) doi:10.1214/24-BA1467.
- H. Häggström, P. Rodrigues, G. Oudoumanessah, F. Forbes and U. Picchini (2024). Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings, Transactions on Machine Learning Research, https://openreview.net/forum?id=Q0nzpRcwWn.
- U. Picchini and M. Tamborrino (2024). Guided sequential ABC schemes for intractable Bayesian models. Bayesian Analysis, doi:10.1214/24-BA1451.
- 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.
- 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.
- 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]
- 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]
- 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]
- 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.
- 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].
- 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].
- 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].
- 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.
- 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.
- U. Picchini (2014). Inference for SDE models via Approximate Bayesian Computation. Journal of Computational and Graphical Statistics, 23(4), 1080-1100. [code].
- U. Picchini and S. Ditlevsen (2011). Practical estimation of high dimensional stochastic differential mixed-effects models. Computational Statistics & Data Analysis, 55(3), 1426-1444.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- U. Picchini, S. Ditlevsen and A. De Gaetano (2005). System noise modelization in glucose/insulin dynamics. Technical Report R.630, IASI-CNR, Rome, Italy.
- 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.
- U. Picchini (2007). Stochastic Differential Models with Applications to Physiology. Department of Statistics, Probability and Applied Statistics, University of Rome "La Sapienza".
Working papers
Research reports
My PhD dissertation