Umberto Picchini
SENIOR LECTURER
Applied Mathematics andAbout 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.
See also http://www.chalmers.se/en/Staff/Pages/picchini.aspx
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Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal
algorithms
S Wiqvist, A Golightly, A.T. McLean, Umberto Picchini
Computational Statistics & Data Analysis - 2021-01-01 -
Partially Exchangeable Networks and architectures for learning summary statistics in Approximate Bayesian
Computation
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen
Proceedings of the 36th International Conference on Machine Learning - 2019-01-01 -
Bayesian inference for stochastic differential equation mixed effects models of a tumor xenography
study
Umberto Picchini, Julie Lyng Forman
Journal of the Royal Statistic Society, Series C: Applied Statistics - 2019-01-01 -
Accelerating delayed-acceptance Markov chain Monte Carlo
algorithms
Samuel Wiqvist, Umberto Picchini, Julie Lyng Forman, Kresten Lindorff-Larsen, Wouter Boomsma
- 2019-01-01