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We introduce the use of the Zig-Zag sampler to the problem of sampling conditional diffusion processes (diffusion bridges).
“Simulating bridges using confluent diffusions”, 2019
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Christophe Andrieu and Samuel Livingstone · 1906
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Henry McKean · 1969
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Simon Duane, A.D. Kennedy, Brian. Pendleton and Duncan Roweth · 1987
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Gareth Roberts and Richard Tweedie · 1996
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“Optimal scaling of discrete approximations to Langevin diffusions”
Gareth Roberts and Jeffrey Rosenthal · 1998
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“Analysis of a nonreversible Markov chain sampler”
Persi Diaconis, Susan Holmes and Radford Neal · 2000
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“Probability and random processes”
Geoffrey Grimmett and David Stirzaker · 2001
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“On inference for partially observed nonlinear diffusion models using the Metropolis–Hastings algorithm”
Gareth Roberts and Osnat Stramer · 2001
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“Introduction to stochastic calculus with applications”
Fima Klebaner · 2005
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“Retrospective exact simulation of diffusion sample paths with applications”
Alexandros Beskos, Omiros Papaspiliopoulos and Gareth Roberts · 2006
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Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani and Gareth Roberts · 2006
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“Rejection-free Monte Carlo sampling for general potentials”
E.. J.. Peters and G. de With · 2012
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“Statistics of Random Processes: I. General Theory”, Stochastic Modelling and Applied Probability
R.S. Liptser, B. Aries and A.N. Shiryaev · 2013
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“Simple simulation of diffusion bridges with application to likelihood inference for diffusions”
“High-dimensional scaling limits of piecewise deterministic sampling algorithms”, 2018
Joris Bierkens, Kengo Kamatani and Gareth. Roberts · 2018
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“All-atom computations with irreversible Markov chains”
Michael. Faulkner, Liang Qin, A.. Maggs and Werner Krauth · 2018
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“Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo”
Paul Fearnhead, Joris Bierkens, Murray Pollock and Gareth. Roberts · 2018
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“Turing: a language for flexible probabilistic inference”
Hong Ge, Kai Xu and Zoubin Ghahramani · 2018
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“Adaptive nonparametric drift estimation for diffusion processes using Faber–Schauder expansions”
Frank van Meulen, Moritz Schauer and Jan van Waaij · 2018
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“The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.”
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“The Zig-Zag process and super-efficient sampling for Bayesian analysis of big data”
Joris Bierkens, Paul Fearnhead and Gareth Roberts · 2019
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Joris Bierkens, Frank van Meulen and Moritz Schauer · 2019
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Manon Michel, Xiaojun Tan and Youjin Deng · 2019
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“Continuous-discrete smoothing of diffusions”, 2020
Marcin Mider, Moritz Schauer and Frank van Meulen · 2020
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Pierre Monmarché, Jérémy Weisman, Louis Lagardère and Jean-Philip Piquemal · 2020
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“ZigZagBoomerang: v0.5.3” https://www.github.com/mschauer/ZigZagBoomerang.jl
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