Umberto Picchini

Studierektor at Applied Mathematics and Statistics

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.

Source: chalmers.se
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Showing 12 publications

2023

JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models

Stefan T. Radev, Marvin Schmitt, Valentin Pratz et al
Proceedings of Machine Learning Research. Vol. 216, p. 1695-1706
Paper in proceeding
2023

Sequentially Guided MCMC Proposals for Synthetic Likelihoods and Correlated Synthetic Likelihoods

Umberto Picchini, Umberto Simola, Jukka Corander
Bayesian Analysis. Vol. 18 (4), p. 1099-1129
Journal article
2023

Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality

Konstantinos Konstantinou, Farnaz Ghorbanpour, Umberto Picchini et al
Statistics in Medicine. Vol. 42 (23), p. 4128-4146
Journal article
2023

Guided sequential ABC schemes for intractable Bayesian models

Umberto Picchini, Massimiliano Tamborrino
Preprint
2022

Scalable and flexible inference framework for stochastic dynamic single-cell models

Sebastian Persson, Niek Welkenhuysen, Sviatlana Shashkova et al
PLoS Computational Biology
Journal article
2021

Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms

Samuel Wiqvist, Andrew Golightly, Ashleigh T. McLean et al
Computational Statistics and Data Analysis. Vol. 157
Journal article
2021

Sequential Neural Posterior and Likelihood Approximation

Samuel Wiqvist, Jes Frellsen, Umberto Picchini
Preprint
2019

Partially exchangeable networks and architectures for learning summary statistics in approximate Bayesian computation

Samuel Wiqvist, Pierre Alexandre Mattei, Umberto Picchini et al
36th International Conference on Machine Learning, ICML 2019. Vol. 2019-June, p. 11795-11804
Paper in proceeding
2019

Bayesian inference for stochastic differential equation mixed effects models of a tumor xenography study

Umberto Picchini, Julie Lyng Forman
Journal of the Royal Statistical Society. Series C: Applied Statistics. Vol. 68 (4), p. 887-913
Journal article
2019

Accelerating delayed-acceptance Markov chain Monte Carlo algorithms

Umberto Picchini, Samuel Wiqvist, Julie Lyng Forman et al
Preprint

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Showing 2 research projects

2020–2024

Deep learning and likelihood-free Bayesian inference for stochastic modelling

Umberto Picchini Applied Mathematics and Statistics
Petar Jovanovski Applied Mathematics and Statistics
Swedish Research Council (VR)
Chalmers AI Research Centre (CHAIR)

7 publications exist
2014–2019

Statistical Inference and Stochastic Modelling of Protein Folding

Umberto Picchini Applied Mathematics and Statistics
Swedish Research Council (VR)

3 publications exist
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