11 papers
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…
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…
Sampling through iterated approximation: Gradient-free and multi-fidelity Bayesian inference via transport
Daniel Sharp, Bart van Bloemen Waanders, Youssef Marzouk
We develop an iterative framework for Bayesian inference problems where the posterior distribution may involve computationally intensive models, intractable gradients, significant…
Neural Triangular Transport Maps: A New Approach Towards Sampling in Lattice QCD
Andrey Bryutkin, Youssef Marzouk
Lattice field theories are fundamental testbeds for computational physics; yet, sampling their Boltzmann distributions remains challenging due to multimodality and long-range corre…
An Eulerian Perspective on Straight-Line Sampling
Panos Tsimpos, Youssef Marzouk
We study dynamic measure transport for generative modeling: specifically, flows induced by stochastic processes that bridge a specified source and target distribution. The conditio…
Canonical Bayesian Linear System Identification
Andrey Bryutkin, Matthew E. Levine, Iñigo Urteaga +1
Standard Bayesian approaches for linear time-invariant (LTI) system identification are hindered by parameter non-identifiability; the resulting complex, multi-modal posteriors make…
Priorconditioned Sparsity-Promoting Projection Methods for Deterministic and Bayesian Linear Inverse Problems
Jonathan Lindbloom, Mirjeta Pasha, Jan Glaubitz +1
High-quality reconstructions of signals and images with sharp edges are needed in a wide range of applications. To overcome the large dimensionality of the parameter space and the…
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…
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…