Umberto Picchini
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.
Showing 12 publications
Guided sequential ABC schemes for intractable Bayesian models
Towards data-conditional simulation for ABC inference in stochastic differential equations
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality
Sequentially Guided MCMC Proposals for Synthetic Likelihoods and Correlated Synthetic Likelihoods
Scalable and flexible inference framework for stochastic dynamic single-cell models
Sequential Neural Posterior and Likelihood Approximation
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms
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Showing 2 research projects
Deep learning and likelihood-free Bayesian inference for stochastic modelling
Statistical Inference and Stochastic Modelling of Protein Folding