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Found 10 papers, 6 papers with code
On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design
The gradient information is often needed for efficient EIG optimization, and as a result the ability to estimate the gradient of EIG is essential for BED problems.
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems
We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements.
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solution.
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
To address these challenges, we propose a novel approximation method for estimating the high-dimensional posterior distribution.
ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics
Predicting the behaviors of pedestrian crowds is of critical importance for a variety of real-world problems.
Affine-Mapping based Variational Ensemble Kalman Filter
We propose an affine-mapping based variational Ensemble Kalman filter for sequential Bayesian filtering problems with generic observation models.
An approximate KLD based experimental design for models with intractable likelihoods
Data collection is a critical step in statistical inference and data science, and the goal of statistical experimental design (ED) is to find the data collection setup that can provide most information for the inference.
Bayesian optimization with local search
Global optimization finds applications in a wide range of real world problems.
Bayesian inverse regression for dimension reduction with small datasets
We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors $\-x$ which can retain the statistical relationship between $\-x$ and the response variable $y$.
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
In particular, we write the joint density approximately as a product of an approximate posterior density and an exponentiated GP surrogate.
