27 citations · 81 across the 21 of their papers we have counts for
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stat.CO2020
MCMC Algorithms for Posteriors on Matrix Spaces
Alexandros Beskos, Kengo Kamatani
We study Markov chain Monte Carlo (MCMC) algorithms for target distributions defined on matrix spaces. Such an important sampling problem has yet to be analytically explored. We ca…
stat.CO2020★ 6 cited
The Boomerang Sampler
Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani +1
This paper introduces the Boomerang Sampler as a novel class of continuous-time non-reversible Markov chain Monte Carlo algorithms. The methodology begins by representing the targe…
stat.CO2020
Non-reversible guided Metropolis kernel
Kengo Kamatani, Xiaolin Song
We construct a class of non-reversible Metropolis kernels as a multivariate extension of the guided-walk kernel proposed by Gustafson 1998. The main idea of our method is to introd…