139 citations · 616 across the 32 of their papers we have counts for
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Strong Invariance Principles for Ergodic Markov Processes
Ardjen Pengel, Joris Bierkens
Strong invariance principles describe the error term of a Brownian approximation of the partial sums of a stochastic process. While these strong approximation results have many app…
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…
Sticky PDMP samplers for sparse and local inference problems
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen +1
We construct a new class of efficient Monte Carlo methods based on continuous-time piecewise deterministic Markov processes (PDMPs) suitable for inference in high dimensional spars…