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Umberto Picchini
Professor
Applied Mathematics and StatisticsAbout Umberto Picchini
I am interested in statistical inference for stochastic modelling, and especially Bayesian computational methods. For example, I am interested in MCMC, sequential Monte Carlo (particle filters) and especially “likelihood-free” methods, such as approximate Bayesian computation (ABC). I have special interest in stochastic modelling (e.g. stochastic differential equations) and applications in biomedicine. More info at https://umbertopicchini.github.io/
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Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear
mappings
Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini
Transactions on Machine Learning Research - 2024 -
Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential
Equations
Petar Jovanovski, Andrew Golightly, Umberto Picchini
Bayesian Analysis - 2024 -
Guided sequential ABC schemes for intractable Bayesian
models
Umberto Picchini, Massimiliano Tamborrino
Bayesian Analysis - 2024 -
Sequentially guided MCMC proposals for synthetic likelihoods and correlated synthetic
likelihoods
Umberto Picchini, Umberto Simola, Jukka Corander
Bayesian Analysis - 2023 -
Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve
mortality
Konstantinos Konstantionu, Farnaz Ghorbanpour, Umberto Picchini, Adam Loavenbruck, Aila Särkkä
Statistics in Medicine - 2023 -
JANA: Jointly Amortized Neural Approximation of Complex Bayesian
Models
Stefan Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Koethe, Paul Buerkner
The 39th Conference on Uncertainty in Artificial Intelligence - 2023 -
Scalable and flexible inference framework for stochastic dynamic single-cell
models
Sebastian Persson, Niek Welkenhuysen, Sviatlana Shashkova, Samuel Wiqvist, Patrick Reith, Gregor W. Schmidt, Umberto Picchini, Marija Cvijovic
PLoS Computational Biology - 2022 -
Sequential neural posterior and likelihood
approximation
Samuel Wiqvist, Jes Frellsen, Umberto Picchini
2021 -
Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal
algorithms
S Wiqvist, A Golightly, A.T. McLean, Umberto Picchini
Computational Statistics & Data Analysis - 2021 -
Bayesian inference for stochastic differential equation mixed effects models of a tumor xenography
study
Umberto Picchini, Julie Lyng Forman
Journal of the Royal Statistic Society, Series C: Applied Statistics - 2019 -
Partially Exchangeable Networks and architectures for learning summary statistics in Approximate Bayesian
Computation
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen
Proceedings of the 36th International Conference on Machine Learning - 2019 -
Accelerating delayed-acceptance Markov chain Monte Carlo
algorithms
Samuel Wiqvist, Umberto Picchini, Julie Lyng Forman, Kresten Lindorff-Larsen, Wouter Boomsma
2019