Umberto Picchini

Senior Lecturer at Applied Mathematics and Statistics

I am Associate Professor in Mathematical Statistics at the Department of Mathematical Sciences, Gothenburg University and Chalmers University of Technology. My research is mainly devoted to statistical inference for dynamical systems and stochastic processes, especially stochastic differential equations (SDEs). Applied work focuses on stochastic mathematical modelling of biomedical issues. My research topics are: Monte Carlo methods in Statistics, Bayesian inference, approximate Bayesian computation (ABC); methods for intractable likelihoods; inference for hierarchical (mixed-effects) models defined by SDEs.

Source: orcid.org
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Showing 4 publications

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
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
Journal article
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

Accelerating delayed-acceptance Markov chain Monte Carlo algorithms

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

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