139 citations · 616 across the 32 of their papers we have counts for
12 papers · 1 filter
Scaling Results for Piecewise Deterministic Monte Carlo : A Survey
Joris Bierkens
Piecewise Deterministic Monte Carlo (PDMC) algorithms utilize continuous time Markov processes to generate samples from continuous distributions, and provide a modern alternative t…
Piecewise Deterministic Markov Processes for Bayesian Inference of PDE Coefficients
Leon Riccius, Iuri B. C. M. Rocha, Joris Bierkens +2
We develop a general framework for piecewise deterministic Markov process (PDMP) samplers that enables efficient Bayesian inference in non-linear inverse problems with expensive li…
Transient regime of piecewise deterministic Monte Carlo algorithms
Sanket Agrawal, Joris Bierkens, Kengo Kamatani +1
Piecewise Deterministic Markov Processes (PDMPs) such as the Bouncy Particle Sampler and the Zig-Zag Sampler, have gained attention as continuous-time counterparts of classical Mar…
Large sample scaling analysis of the Zig-Zag algorithm for Bayesian inference
Sanket Agrawal, Joris Bierkens, Gareth O. Roberts
Piecewise deterministic Markov processes provide scalable methods for sampling from the posterior distributions in big data settings by admitting principled sub-sampling strategies…
Methods and applications of PDMP samplers with boundary conditions
Joris Bierkens, Sebastiano Grazzi, Gareth Roberts +1
We extend Monte Carlo samplers based on piecewise deterministic Markov processes (PDMP samplers) by formally defining different boundary conditions such as sticky floors, soft and…
Federated Bayesian Computation via Piecewise Deterministic Markov Processes
Joris Bierkens, Andrew Duncan
When performing Bayesian computations in practice, one is often faced with the challenge that the constituent model components and/or the data are only available in a distributed f…