## practical PDMP

**W**hile in Warwick, last month, I attended a reading group on PDMPs where Filippo Pagani talked about *practical* PDMP, connected with a recent arXival by Bertazzi, Bierkens and Dobson. The central question when implementing PDMP is to find a realistic way of solving

to decide on the stopping time (when the process ceases to be deterministic). The usual approach is to use Poisson thinning by finding an upper bound on λ, but this is either difficult or potentially inefficient (and sometimes both).

“finding a sharp bound M(s)[for Poisson thinning]can be an extremely challenging problem in most practical settings (…) In order to overcome this problem, we introduce discretisation schemes for PDMPs which make their

approximate simulation possible.”

Some of the solutions proposed in Bertazzi et al. are relying on

- using a frozen (fixed) λ
- discretising time and the integral (first order scheme)
- allowing for more than a jump over a time interval (higher order schemes)
- going through control variates (when gradient is Lipschitz and Hessian bounded, with known constants) as it produces a linear rate λ
- subsampling (at least for Zig Zag)

with theoretical guarantees that the approximations are convergent, as the time step goes to zero. They (almost obviously) remain model dependent solutions (with illustrations for the Zig Zag and bouncy particle versions), with little worse case scenarios, but this is an extended investigation into making PDMPs more manageable!

December 9, 2021 at 7:57 am

Je suis dubitatif… l’avantage d’un PDMP c’est notamment la simulation exacte…

December 11, 2021 at 9:52 am

Exacte? A partir de quand?