This is the personal page of Umberto Picchini, an Associate Professor in Mathematical Statistics at the Department of Mathematical Sciences at Chalmers University of Technology and University of Gothenburg, Sweden. Here is my official university profile. I am also a faculty member of the Chalmers AI Research Centre.
Let’s get in touch to talk about statistical inference (especially Bayesian), likelihood-free methods for models with intractable likelihoods, and Monte Carlo statistical methods such as MCMC and sequential Monte Carlo, and the application of statistical inference in applied problems.
Interested in Bayesian methods? Check out this page!
- July 2022: I am the chair of the local organization for NORDSTAT 2023. Check out the event’s page at nordstat2023.org.
- July 2022: I am member of the scientific committee for the BayesComp 2023 satellite workshop “Bayesian computing without exact likelihoods” bayescomp2023.
- 27 June 2022: New paper: Guided sequential ABC schemes for intractable Bayesian models.
- June 2022: giving a talk at ISBA in Montreal on our Sequentially guided synthetic likelihoods paper on 28 June in the 1.30pm session. But there is also a poster here.
- May 2022: published on PLOS Comp. Biology: PEPSDI framework for Bayesian inference for mixed-effects stochastic models.
- 1 April 2022: here are slides from my talk at Maths department in Bristol on our PEPSDI work and generally SDE mixed-effects models.
- 8 February 2022: published on Bayesian Analysis, Sequentially guided MCMC proposals for synthetic likelihoods and correlated synthetic likelihoods.
- 24 September 2021: Samuel Wiqvist defends his PhD thesis.
- 4 July 2021: New paper “PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models”, bioRxiv.
- 2 July 2021: paper significantly updated “Stratified sampling and bootstrapping for approximate Bayesian computation”.
- 23 June -2 July 2021: I gave a talk at ISBA 2021 entitled “Guided sequential menthods for intractable Bayesian models”.