Generalized Bouncy Particle Sampler
classification
📊 stat.CO
keywords
bouncyparticlesamplerdeterministicgbpsmarkovtransitionalgorithm
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As a special example of piecewise deterministic Markov process, bouncy particle sampler is a rejection-free, irreversible Markov chain Monte Carlo algorithm and can draw samples from target distribution efficiently. We generalize bouncy particle sampler in terms of its transition dynamics. In BPS, the transition dynamic at event time is deterministic, but in GBPS, it is random. With the help of this randomness, GBPS can overcome the reducibility problem in BPS without refreshement.
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Cited by 1 Pith paper
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Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Introduces an adaptive thinning scheme to make PDMP-based MCMC feasible for Bayesian inference in neural networks by handling model-specific IPPs efficiently.
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