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- W4382491736 abstract "Monte Carlo methods - such as Markov chain Monte Carlo (MCMC) and piecewise deterministic Markov process (PDMP) samplers - provide asymptotically exact estimators of expectations under a target distribution. There is growing interest in alternatives to this asymptotic regime, in particular in constructing estimators that are exact in the limit of an infinite amount of computing processors, rather than in the limit of an infinite number of Markov iterations. In particular, Jacob et al. (2020) introduced coupled MCMC estimators to remove the non-asymptotic bias, resulting in MCMC estimators that can be embarrassingly parallelised. In this work, we extend the estimators of Jacob et al. (2020) to the continuous-time context and derive couplings for the bouncy, the boomerang and the coordinate samplers. Some preliminary empirical results are included that demonstrate the reasonable scaling of our method with the dimension of the target." @default.
- W4382491736 created "2023-06-29" @default.
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- W4382491736 date "2023-06-27" @default.
- W4382491736 modified "2023-09-24" @default.
- W4382491736 title "Debiasing Piecewise Deterministic Markov Process samplers using couplings" @default.
- W4382491736 doi "https://doi.org/10.48550/arxiv.2306.15422" @default.
- W4382491736 hasPublicationYear "2023" @default.
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