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08/19/2023
On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design
Bayesian Experimental Design (BED), which aims to find the optimal exper...
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07/30/2023
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems
Combining the strengths of model-based iterative algorithms and data-dri...
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04/19/2023
Entropy Estimation via Uniformization
Entropy estimation is of practical importance in information theory and ...
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04/17/2023
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
Bayesian methods for solving inverse problems are a powerful alternative...
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04/10/2023
Approximate Primal-Dual Fixed-Point based Langevin Algorithms for Non-smooth Convex Potentials
The Langevin algorithms are frequently used to sample the posterior dist...
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03/27/2023
Parameter estimation for many-particle models from aggregate observations: A Wasserstein distance based sequential Monte Carlo sampler
In this work we study systems consisting of a group of moving particles....
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02/22/2023
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
Solving high-dimensional Bayesian inverse problems (BIPs) with the varia...
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02/14/2023
Adaptive design of experiment via normalizing flows for failure probability estimation
Failure probability estimation problem is an crucial task in engineering...
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11/08/2022
Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions
In many Bayesian inverse problems the goal is to recover a spatially var...
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10/18/2022
ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics
Predicting the behaviors of pedestrian crowds is of critical importance ...
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09/30/2022
Multicanonical Sequential Monte Carlo Sampler for Uncertainty Quantification
In many real-world engineering systems, the performance or reliability o...
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03/10/2021
Linear-Mapping based Variational Ensemble Kalman Filter
We propose a linear-mapping based variational Ensemble Kalman filter for...
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02/21/2021
Inverse Gaussian Process regression for likelihood-free inference
In this work we consider Bayesian inference problems with intractable li...
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12/16/2020
Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference
Many real-world problems require one to estimate parameters of interest,...
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04/24/2020
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels
By facilitating the generation of samples from arbitrary probability dis...
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04/01/2020
An approximate KLD based experimental design for models with intractable likelihoods
Data collection is a critical step in statistical inference and data sci...
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11/20/2019
Bayesian optimization with local search
Global optimization finds applications in a wide range of real world pro...
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10/11/2019
Maximizing conditional entropy of Hamiltonian Monte Carlo sampler
The performance of Hamiltonian Monte Carlo (HMC) sampler depends critica...
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08/03/2019
A hierarchical neural hybrid method for failure probability estimation
Failure probability evaluation for complex physical and engineering syst...
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06/19/2019
Bayesian inverse regression for supervised dimension reduction with small datasets
We consider supervised dimension reduction problems, namely to identify ...
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03/05/2019
Bayesian inference and uncertainty quantification for image reconstruction with Poisson data
We provide a complete framework for performing infinite-dimensional Baye...
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11/23/2018
A weight-bounded importance sampling method for variance reduction
Importance sampling (IS) is an important technique to reduce the estimat...
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10/20/2018
A defensive marginal particle filtering method for data assimilation
The Particle filtering (PF) method is often used to estimate the states ...
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03/29/2017