6 papers
Deep unfolding of MCMC kernels: scalable, modular & explainable GANs for high-dimensional posterior sampling
Jonathan Spence, Tobías I. Liaudat, Konstantinos Zygalakis +1
Markov chain Monte Carlo (MCMC) methods are fundamental to Bayesian computation, but can be computationally intensive, especially in high-dimensional settings. Push-forward generat…
Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities
Takuo Matsubara, Andrew Duncan, Simon Cotter +1
We propose bandit importance sampling (BIS), a powerful importance sampling framework tailored for settings in which evaluating the target density is computationally expensive. BIS…
Piecewise Deterministic Sampling for Constrained Distributions
Joël Tatang Demano, Paul Dobson, Konstantinos Zygalakis
In this paper, we propose a novel class of Piecewise Deterministic Markov Processes (PDMPs) that are designed to sample from probability distributions supported on a convex set…
Hypothesis Testing in Imaging Inverse Problems
Yiming Xi, Konstantinos Zygalakis, Marcelo Pereyra
This paper proposes a framework for semantic hypothesis testing tailored to imaging inverse problems. Modern imaging methods struggle to support hypothesis testing, a core componen…
Efficient Bayesian Computation Using Plug-and-Play Priors for Poisson Inverse Problems
Teresa Klatzer, Savvas Melidonis, Marcelo Pereyra +1
This paper studies plug-and-play (PnP) Langevin sampling strategies for Bayesian inference in low-photon Poisson imaging problems, a challenging class of problems with significant…
Bayesian computation with generative diffusion models by Multilevel Monte Carlo
Abdul-Lateef Haji-Ali, Marcelo Pereyra, Luke Shaw +1
Generative diffusion models have recently emerged as a powerful strategy to perform stochastic sampling in Bayesian inverse problems, delivering remarkably accurate solutions for a…