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Title: Pseudo-marginal piecewise deterministic Monte Carlo Authors:  Giorgos Vasdekis - University College London (United Kingdom) [presenting]
Abstract: Piecewise Deterministic Markov Processes (PDMPs) have recently caught the attention of the MCMC community for having a non-diffusive behavior, potentially allowing them to explore the state space efficiently. This makes them good candidates for generating MCMC algorithms. One important problem in Bayesian computation is inference for models where the pointwise evaluation of the posterior is not available, but one has access to an unbiased estimator of the posterior. A technique to deal with this problem is the Pseudo-marginal Metropolis-Hastings algorithm. We describe a PDMP algorithm that can be used in the same posterior free setting and can be seen as the analogue of Pseudo-marginal for Piecewise Deterministic Monte Carlo. We show that the algorithm targets the posterior of interest. We also provide some numerical examples, focusing on the case of Approximate Bayesian Computation (ABC), a popular method to deal with problems in the setting of likelihood-free inference.