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Umberto Picchini
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Associate Professor in Mathematical Statistics
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Interested in statistical inference and computational statistics for stochastic processes and stochastic modelling.
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Presentations
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Guided sequential ABC schemes for simulation-based inference
Guided sequential ABC schemes for simulation-based inference
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Bayesian inference for mixed-effects models driven by SDEs and other stochastic models: a scalable approach
Bayesian inference for mixed-effects models driven by SDEs and other stochastic models: a scalable approach
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Stratified Monte Carlo and bootstrapping for approximate Bayesian computation
Stratified Monte Carlo and bootstrapping for approximate Bayesian computation
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Stratified sampling and resampling for approximate Bayesian computation
Stratified sampling and resampling for approximate Bayesian computation
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A likelihood-free version of the stochastic approximation EM algorithm (SAEM) for parameter estimation in complex models
A likelihood-free version of the stochastic approximation EM algorithm (SAEM) for parameter estimation in complex models
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Inference via Bayesian Synthetic Likelihoods for a Mixed-Effects SDE Model of Tumor Growth
Inference via Bayesian Synthetic Likelihoods for a Mixed-Effects SDE Model of Tumor Growth
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