Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Abstract
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how they can be implemented in settings where the parameters live on a restricted domain.
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