arXiv research

Estimates high-dimensional posterior densities by marginal distributions and neural networks.

Improved sampling for high-dimensional posteriors with underdamped Langevin.

Develops a fast variational approximation for high-dimensional empirical Bayes posteriors.

GATSBI uses GANs for SBI, improving posterior estimation in high dimensions.

The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.

High-dimensional VAEs inevitably collapse to prior, requiring large datasets for good performance.

We propose a novel approach to approximate complex high-dimensional posteriors using neural samplers.

MsIGN tackles high-dimensional Bayesian inference using multiscale structure.

Bayesian inference corrected for bias in high-dimensional models.

New framework improves variational inference for high-dimensional posteriors.

A new framework for clustering high-dimensional data using vertical shards.

A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.

Machine learning techniques improve Bayesian computation for complex data.

TARP tests accuracy of generative posterior estimators.

Study shows TAP free energy minimization provides better posterior inference in high-dimensional linear models.

Study high-dimensional Bayesian linear regression using variational inference.

This paper introduces NPR, a technique to improve Bayesian inference for multi-modal, high-dimensional simulations.

DiBO uses diffusion models to optimize high-dimensional black-box functions efficiently.

Stable training of deep normalizing flows for high-dimensional variational inference.

In the popular approach of "Bayesian variable selection" (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs posterior is con…

New method improves posterior sampling for complex data models.

This work introduces a method for visualizing high-dimensional posteriors using hierarchical tree-valued predictions.

High-dimensional unimodal distributions can cause MCMC methods to fail.

Our article considers a Gaussian variational approximation of the posterior density in a high-dimensional state space model. The variational parameters to be optimized are the mean vector and the covariance matrix of the approximation. The number of parameters in the covariance matrix grows as the square of the number …

Bayesian neural networks achieve optimal posterior contraction rates in Besov spaces with intrinsic dimensionality.

New method improves high-dimensional Bayesian optimization efficiency using MCMC.

Characterizes uncertainty in high-dimensional linear classification models.

ABI bypasses likelihood intractability with nonparametric distribution matching.

We present a Communication-efficient Surrogate Likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation and Bayesian inference. For…

EP method speeds up Bayesian probit regression in high dimensions.

Bayesian learning made scalable with posteriors library.

A new particle filter avoids resampling to improve state estimation in high dimensions.

Empirical Bayes rates via variational approximations and prior decomposition.

New method uses EKI for efficient Bayesian inference in high-dimensional problems.

New method speeds up Bayesian inference for complex simulators.

Bayesian DWD improves classification of high-dimensional data.

Method generates joint posterior samples of source and foreground mass distributions for gravitational lensing.

New method estimates covariance in multi-view data with better accuracy and uncertainty.

How can one perform Bayesian inference on stochastic simulators with intractable likelihoods? A recent approach is to learn the posterior from adaptively proposed simulations using neural network-based conditional density estimators. However, existing methods are limited to a narrow range of proposal distributions or r…

AP-CDE uses NF to estimate high-dimensional conditional densities, improving interpretability.

A new method maps high-dimensional Bayesian inverse problems to lower dimensions.

Variational language models seek to estimate the posterior of latent variables with an approximated variational posterior. The model often assumes the variational posterior to be factorized even when the true posterior is not. The learned variational posterior under this assumption does not capture the dependency relat…

We use neural networks to estimate complex model posteriors efficiently.

We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contribu…

Bayesian Tobit model tackles high-dimensional censored data with Horseshoe prior.

GPNs use unlabeled data to estimate uncertainty in Bayesian problems.

The paper decouples shrinkage and selection in Bayesian Quantile Regression.

Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the …