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We show fundamental properties of the Markov semigroup of recently proposed MCMC algorithms based on Piecewise-deterministic Markov processes (PDMPs) such as the Bouncy Particle Sampler, the Zig-Zag process or the Randomized Hamiltonian Monte Carlo method.
Note on the derivatives with respect to a parameter of the solutions of a system of differential equations
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Christophe Andrieu, Nando De Freitas, Arnaud Doucet, and Michael I Jordan · 2003
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Geometric integrators and the hamiltonian monte carlo method
Nawaf Bou-Rabee and J. M. Sanz-Serna · 2018
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The bouncy particle sampler: a nonreversible rejection-free markov chain monte carlo method
Alexandre Bouchard-Côté, Sebastian J Vollmer, and Arnaud Doucet · 2018
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Markov models & optimization
Mark HA Davis · 2018
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George Deligiannidis, Daniel Paulin, Alexandre Bouchard-Côté, and Arnaud Doucet · 2018
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Cited alongside, same era.
Randomized hamiltonian monte carlo
Nawaf Bou-Rabee and Jesus Maria Sanz-Serna · 2017
Cited alongside, same era.
Piecewise-deterministic markov chain monte carlo
Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, and Arnaud Doucet · 2017
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Alain Durmus, Arnaud Guillin, and Pierre Monmarché · 2018
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Piecewise deterministic markov processes for continuous-time monte carlo
Paul Fearnhead, Joris Bierkens, Murray Pollock, Gareth O Roberts, et al · 2018
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The zig-zag process and super-efficient sampling for bayesian analysis of big data
Joris Bierkens, Paul Fearnhead, Gareth Roberts, et al · 2019
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