11 papers
Efficient sampling for sparse Bayesian learning using hierarchical prior normalization
Jan Glaubitz, Youssef Marzouk
We introduce an approach for efficient Markov chain Monte Carlo (MCMC) sampling for challenging high-dimensional distributions in sparse Bayesian learning (SBL). The core innovatio…
Quantitative Wasserstein Propagation of Chaos for Transport Ensemble Filters
Frederic J. N. Jorgensen, Ricardo Baptista, Franca Hoffmann +1
We develop a general probabilistic framework for analyzing propagation of chaos in transport ensemble filters (TEFs), a broad class of interacting particle systems that are used to…
Dynestyx: A Probabilistic Programming Library for Dynamical Systems
Daniel Waxman, Dmitry Batenkov, John Feser +4
State-space models (SSMs) are the standard formalism for Bayesian treatment of dynamical systems, with natural applications in statistics, signal processing, and machine learning.…
Optimizing Irreversible Perturbations of the Unadjusted Langevin Algorithm
Qianyu Julie Zhu, Youssef Marzouk, Konstantinos Spiliopoulos +1
Irreversible perturbations accelerate the convergence of Langevin dynamics, breaking detailed balance while preserving the invariant measure. The design of optimal irreversible per…
Bayesian optimal experimental design with Wasserstein information criteria
Tapio Helin, Youssef Marzouk, Jose Rodrigo Rojo-Garcia
Bayesian optimal experimental design (OED) provides a principled framework for selecting observations or experiments. We introduce new Bayesian design criteria based on the expecte…
To discretize continually: Mean shift interacting particle systems for Bayesian inference
Ayoub Belhadji, Daniel Sharp, Youssef M. Marzouk
Integration against a probability distribution given its unnormalized density is a central task in Bayesian inference and other fields. We introduce new methods for approximating s…