Importance corrected neural JKO sampling
J Hertrich, R Gruhlke - arXiv preprint arXiv:2407.20444, 2024 - arxiv.org
In order to sample from an unnormalized probability density function, we propose to
combine continuous normalizing flows (CNFs) with rejection-resampling steps based on …
combine continuous normalizing flows (CNFs) with rejection-resampling steps based on …
Stein transport for Bayesian inference
N Nüsken - arXiv preprint arXiv:2409.01464, 2024 - arxiv.org
We introduce $\textit {Stein transport} $, a novel methodology for Bayesian inference
designed to efficiently push an ensemble of particles along a predefined curve of tempered …
designed to efficiently push an ensemble of particles along a predefined curve of tempered …
Efficient, multimodal, and derivative-free bayesian inference with Fisher–Rao gradient flows
In this paper, we study efficient approximate sampling for probability distributions known up
to normalization constants. We specifically focus on a problem class arising in Bayesian …
to normalization constants. We specifically focus on a problem class arising in Bayesian …
[PDF][PDF] A connection between tempering and entropic mirror descent
This paper explores the connections between tempering (for Sequential Monte Carlo; SMC)
and entropic mirror descent to sample from a target probability distribution whose …
and entropic mirror descent to sample from a target probability distribution whose …
Wasserstein gradient flows of MMD functionals with distance kernel and Cauchy problems on quantile functions
We give a comprehensive description of Wasserstein gradient flows of maximum mean
discrepancy (MMD) functionals $\mathcal F_\nu:=\text {MMD} _K^ 2 (\cdot,\nu) $ towards …
discrepancy (MMD) functionals $\mathcal F_\nu:=\text {MMD} _K^ 2 (\cdot,\nu) $ towards …
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
Sequential-in-time methods solve a sequence of training problems to fit nonlinear
parametrizations such as neural networks to approximate solution trajectories of partial …
parametrizations such as neural networks to approximate solution trajectories of partial …
Fisher-rao gradient flow: geodesic convexity and functional inequalities
The dynamics of probability density functions has been extensively studied in science and
engineering to understand physical phenomena and facilitate algorithmic design. Of …
engineering to understand physical phenomena and facilitate algorithmic design. Of …
Ensemble-Based Annealed Importance Sampling
Sampling from a multimodal distribution is a fundamental and challenging problem in
computational science and statistics. Among various approaches proposed for this task, one …
computational science and statistics. Among various approaches proposed for this task, one …
Connections between sequential Bayesian inference and evolutionary dynamics
S Pathiraja, P Wacker - arXiv preprint arXiv:2411.16366, 2024 - arxiv.org
It has long been posited that there is a connection between the dynamical equations
describing evolutionary processes in biology and sequential Bayesian learning methods …
describing evolutionary processes in biology and sequential Bayesian learning methods …
Wasserstein Gradient Flows of MMD Functionals with Distance Kernels under Sobolev Regularization
We consider Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals
$\text {MMD} _K^ 2 (\cdot,\nu) $ for positive and negative distance kernels $ K (x, y):=\pm| xy …
$\text {MMD} _K^ 2 (\cdot,\nu) $ for positive and negative distance kernels $ K (x, y):=\pm| xy …