5 papers
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…
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…
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…
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…
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…