1 citations · 1 across the 6 of their papers we have counts for
6 papers
Differential privacy guarantees of Markov chain Monte Carlo algorithms
Andrea Bertazzi, Tim Johnston, Gareth O. Roberts +1
This paper aims to provide differential privacy (DP) guarantees for Markov chain Monte Carlo (MCMC) algorithms. In a first part, we establish DP guarantees on samples output by MCM…
Sampling with time-changed Markov processes
Andrea Bertazzi, Giorgos Vasdekis
We study time-changed Markov processes to speed up the convergence of Markov chain Monte Carlo (MCMC) algorithms. The time-changed process is defined by adjusting the speed of time…
Piecewise deterministic generative models
Andrea Bertazzi, Dario Shariatian, Umut Simsekli +2
We introduce a novel class of generative models based on piecewise deterministic Markov processes (PDMPs), a family of non-diffusive stochastic processes consisting of deterministi…
Piecewise deterministic sampling with splitting schemes
Andrea Bertazzi, Paul Dobson, Pierre Monmarché
We introduce Markov chain Monte Carlo (MCMC) algorithms based on numerical approximations of piecewise-deterministic Markov processes obtained with the framework of splitting schem…
Approximations of Piecewise Deterministic Markov Processes and their convergence properties
Andrea Bertazzi, Joris Bierkens, Paul Dobson
Piecewise deterministic Markov processes (PDMPs) are a class of stochastic processes with applications in several fields of applied mathematics spanning from mathematical modeling…
Adaptive schemes for piecewise deterministic Monte Carlo algorithms
Andrea Bertazzi, Joris Bierkens
The Bouncy Particle sampler (BPS) and the Zig-Zag sampler (ZZS) are continuous time, non-reversible Monte Carlo methods based on piecewise deterministic Markov processes. Experimen…