[Statlist] Research Webinar in Statistics *FRIDAY 10 DECEMBER 2021* GSEM, University of Geneva

[gsem-support-instituts]

Dear All,

We are pleased to invite you to our next Research Webinar.

Looking forward to seeing you


Organizers :                                                                                   
E. Cantoni - S. Engelke - D. La Vecchia
S. Sperlich - F. Trojani - M.-P. Victoria-Feser


FRIDAY 10 DECEMBER 2021 at 11:15am
ONLINE
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192


Nonreversible MCMC with Constraints or Discontinuities
Jere KOSKELA, University of Warwick, UK, https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/koskela/

ABSTRACT:
MCMC is a standard tool for sampling from unnormalised distributions, which is a common task in Bayesian inference among other settings. A key feature of practical algorithms is that they should explore the support of the target distribution rapidly. Essentially all MCMC methods in wide use are based on reversible random walks, which have an inherent tendency to backtrack in a manner characteristic of diffusions, inhibiting exploration. A recent body of work has introduced several classes of nonreversible MCMC methods based on piecewise deterministic Markov processes, which avoid diffusive backtracking. However, the formulations of these methods require connected state spaces without boundaries, as well as differentiable target densities. I will show that analogous processes can be constructed on state spaces consisting of disconnected regions with boundaries, and for target densities with discontinuities. I will illustrate the construction with sampling from the posterior distribution of the Kingman coalescent model of population genetics, whose support consists of discrete binary tree topologies as well as nonnegative, continuous branch lengths. 


Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/