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.
Showing 9 publications
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
Sequentially Guided MCMC Proposals for Synthetic Likelihoods and Correlated Synthetic Likelihoods
Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality
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