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.

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!


  • 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”.
  • 25 June 2021: significantly updated “Sequentially guided MCMC proposals for synthetic likelihoods and correlated synthetic likelihoods”, arxiv:2004.04558.
  • 9 June 2021: significantly updated “Sequential neural posterior and likelihood approximation”, arxiv:2102.06522.
  • 15 February 2021: New paper “Sequential neural posterior and likelihood approximation”, arxiv:2102.06522.

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