18 citations · 35 across the 11 of their papers we have counts for
11 papers
Hamiltonian dynamics for sampling on discrete spaces
Raphaël Barboni, Sebastiano Grazzi, Giacomo Zanella
We develop a general class of non-reversible Hamiltonian Monte Carlo dynamics on discrete state spaces. The method augments the discrete state with a continuous momentum variable a…
On micromodes in Bayesian posterior distributions and their implications for MCMC
Sanket Agrawal, Sebastiano Grazzi, Gareth O. Roberts
We investigate the existence and severity of local modes in posterior distributions from Bayesian analyses. These are known to occur in posterior tails resulting from heavy-tailed…
Sub-Cauchy Sampling: Escaping the Dark Side of the Moon
Sebastiano Grazzi, Sifan Liu, Gareth O. Roberts +1
We introduce a Markov chain Monte Carlo algorithm based on Sub-Cauchy Projection, a geometric transformation that generalizes stereographic projection by mapping Euclidean space in…
On randomized step sizes in Metropolis-Hastings algorithms
Sebastiano Grazzi, Samuel Livingstone, Lionel Riou-Durand
The performance of Metropolis-Hastings algorithms is highly sensitive to the choice of step size, and miss-specification can lead to severe loss of efficiency. We study algorithms…
A discomfort-informed adaptive Gibbs sampler for finite mixture models
Davide Fabbrico, Andi Q. Wang, Sebastiano Grazzi +5
Finite mixture models are frequently used to uncover latent structures in high-dimensional datasets (e.g.\ identifying clusters of patients in electronic health records). The infer…
Parallel computations for Metropolis Markov chains with Picard maps
Sebastiano Grazzi, Giacomo Zanella
We develop parallel algorithms for simulating zeroth-order (aka gradient-free) Metropolis Markov chains based on the Picard map. For Random Walk Metropolis Markov chains targeting…
Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers
Paul Fearnhead, Sebastiano Grazzi, Chris Nemeth +1
Recent work has suggested using Monte Carlo methods based on piecewise deterministic Markov processes (PDMPs) to sample from target distributions of interest. PDMPs are non-reversi…
Methods and applications of PDMP samplers with boundary conditions
Joris Bierkens, Sebastiano Grazzi, Gareth Roberts +1
We extend Monte Carlo samplers based on piecewise deterministic Markov processes (PDMP samplers) by formally defining different boundary conditions such as sticky floors, soft and…
Sticky PDMP samplers for sparse and local inference problems
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen +1
We construct a new class of efficient Monte Carlo methods based on continuous-time piecewise deterministic Markov processes (PDMPs) suitable for inference in high dimensional spars…
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…
A piecewise deterministic Monte Carlo method for diffusion bridges
Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen +1
We introduce the use of the Zig-Zag sampler to the problem of sampling conditional diffusion processes (diffusion bridges). The Zig-Zag sampler is a rejection-free sampling scheme…