Cover Image

Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Feng, Nan, Huan, Xun
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Computation Methodology 62F15, 65N21, 68T07, 74S05
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bayesian Covariance Uncertainty for Adaptive Pilot-Sampling Termination in Multi-fidelity Uncertainty Quantification
by: Coons, Thomas E., et al.
Published: (2025)

Amortized Energy-Based Bayesian Inference
by: Kaveh, Hojjat, et al.
Published: (2026)

The Bayesian Finite Element Method in Inverse Problems: a Critical Comparison between Probabilistic Models for Discretization Error
by: Poot, Anne, et al.
Published: (2025)

Surrogate-Based Bayesian Inference: Uncertainty Quantification and Active Learning
by: Roberts, Andrew Gerard, et al.
Published: (2026)

Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2026)

A Plug-and-Play Method with Inpainting Network for Bayesian Uncertainty Quantification in Imaging
by: Wang, Xiaoyu, et al.
Published: (2023)

Mean--Variance Risk-Aware Bayesian Optimal Experimental Design for Nonlinear Models
by: Shen, Wanggang, et al.
Published: (2026)

Bayesian--AI Fusion for Epidemiological Decision Making: Calibrated Risk, Honest Uncertainty, and Hyperparameter Intelligence
by: Chatterjee, Debashis
Published: (2025)

Posterior Bayesian Neural Networks with Dependent Weights
by: Apollonio, Nicola, et al.
Published: (2025)

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
by: Aich, Agnideep, et al.
Published: (2025)

Bayesian ICA with super-Gaussian Source Priors
by: Datta, Jyotishka, et al.
Published: (2024)

Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning
by: Zou, Zongren, et al.
Published: (2024)

Neural network solvers for parametrized elasticity problems that conserve linear and angular momentum
by: Boon, Wietse M., et al.
Published: (2024)

Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
by: Gyger, Tim, et al.
Published: (2024)

Computational homogenization for aerogel-like polydisperse open-porous materials using neural network--based surrogate models on the microscale
by: Klawonn, Axel, et al.
Published: (2024)

Bayesian parameter identification in impedance boundary conditions for Helmholtz problems
by: Wulbusch, Nick, et al.
Published: (2023)

Adaptive Gaussian Process Regression for Bayesian inverse problems
by: Villani, Paolo, et al.
Published: (2024)

Wavenumber-Explicit Well-Posedness of Bayesian Shape Inversion in Acoustic Scattering
by: Kuijpers, Safiere, et al.
Published: (2024)

Variable Fusion and Selection via a Spike-and-Slab Approach with Nonlocal Priors
by: Miyake, Junya, et al.
Published: (2026)

Quasi-Monte Carlo for Bayesian design of experiment problems governed by parametric PDEs
by: Kaarnioja, Vesa, et al.
Published: (2024)

Laplace Variational Inference for Bayesian Envelope Models
by: Kim, Seunghyeon, et al.
Published: (2026)

A Gaussian process and linear-based framework for computing cut distributions in modular Bayesian calibration of two chained computer models
by: Baldé, Oumar, et al.
Published: (2023)

Hybridizable Discontinuous Galerkin Methods for Coupled Poro-Viscoelastic and Thermo-Viscoelastic Systems
by: Meddahi, Salim
Published: (2025)

Predictive Inference via Kernel Density Estimates
by: Hilbert, Torey
Published: (2026)

Inverse Probability Weighting: from Survey Sampling to Evidence Estimation
by: Datta, Jyotishka, et al.
Published: (2022)

Testing-driven Variable Selection in Bayesian Modal Regression
by: Duan, Jiasong, et al.
Published: (2025)

A Bayesian Optimal Phase II Design for Randomized Immunotherapy Trials with Delayed Treatment Effects
by: Cai, Zhongheng, et al.
Published: (2025)

Quasi-Monte Carlo and importance sampling methods for Bayesian inverse problems
by: He, Zhijian, et al.
Published: (2024)

Exact Bayesian Planning for Simple Step-Stress Accelerated Life Testing with Competing Risks
by: Prajapat, Kiran
Published: (2026)

Semiparametric Uncertainty Quantification via Isotonized Posterior for Deconvolutions
by: Gili, Francesco, et al.
Published: (2026)

Nonparametric Prior Learning in Differential Equation Modeling
by: Jia, Junxiong, et al.
Published: (2023)

An energy-based deep splitting method for the nonlinear filtering problem
by: Bågmark, Kasper, et al.
Published: (2022)

Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI
by: Terhag, F., et al.
Published: (2023)

Joint Signal Recovery and Uncertainty Quantification via the Residual Prior Transform
by: Xiao, Yao, et al.
Published: (2025)

Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
by: Zhong, Shijie, et al.
Published: (2024)

Quantile Importance Sampling
by: Datta, Jyotishka, et al.
Published: (2023)

A new sparsity promoting residual transform operator for Lasso regression
by: Xiao, Yao, et al.
Published: (2025)

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
by: Galvão, Luciano Ribeiro, et al.
Published: (2025)

A Generalized Weak Galerkin Method for Linear Elasticity with Nonpolynomial Approximations
by: Wang, Junping, et al.
Published: (2026)

Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
by: Wang, Yueyang, et al.
Published: (2026)