Image de couverture de livre

Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Yang, Huchen, Dong, Xinghao, Wu, Jinlong
Format:	Preprint
Publié:	2026
Sujets:	Information Theory Machine Learning 62K05, 62F15, 65C05
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Mean--Variance Risk-Aware Bayesian Optimal Experimental Design for Nonlinear Models
par: Shen, Wanggang, et autres
Publié: (2026)

Goal oriented optimal design of infinite-dimensional Bayesian inverse problems using quadratic approximations
par: Neuberger, J. Nicholas, et autres
Publié: (2024)

Large sample scaling analysis of the Zig-Zag algorithm for Bayesian inference
par: Agrawal, Sanket, et autres
Publié: (2024)

Subspace accelerated measure transport methods for fast and scalable sequential experimental design, with application to photoacoustic imaging
par: Cui, Tiangang, et autres
Publié: (2025)

On micromodes in Bayesian posterior distributions and their implications for MCMC
par: Agrawal, Sanket, et autres
Publié: (2026)

A global optimum-informed greedy algorithm for A-optimal experimental design
par: Aarset, Christian
Publié: (2024)

Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty
par: Alexanderian, Alen, et autres
Publié: (2022)

An optimal experimental design approach to sensor placement in continuous stochastic filtering
par: Pathiraja, Sahani, et autres
Publié: (2025)

Bayesian Covariance Uncertainty for Adaptive Pilot-Sampling Termination in Multi-fidelity Uncertainty Quantification
par: Coons, Thomas E., et autres
Publié: (2025)

Inverse Probability Weighting: from Survey Sampling to Evidence Estimation
par: Datta, Jyotishka, et autres
Publié: (2022)

Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems
par: Gruhlke, Robert, et autres
Publié: (2025)

Taylor Approximation Variance Reduction for Approximation Errors in PDE-constrained Bayesian Inverse Problems
par: Nicholson, Ruanui, et autres
Publié: (2025)

Robust optimal design of large-scale Bayesian nonlinear inverse problems
par: Chowdhary, Abhijit, et autres
Publié: (2024)

Mixture-Weighted Ensemble Kalman Filter with Quasi-Monte Carlo Transport
par: Klebanov, Ilja, et autres
Publié: (2026)

A Multi-fidelity Estimator of the Expected Information Gain for Bayesian Optimal Experimental Design
par: Coons, Thomas E., et autres
Publié: (2025)

Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
par: Shen, Wanggang, et autres
Publié: (2021)

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

Bayesian D-Optimal Experimental Designs via Column Subset Selection
par: Eswar, Srinivas, et autres
Publié: (2024)

Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis
par: Chen, Qiao, et autres
Publié: (2024)

Record-based transmuted unit omega distribution: different methods of estimation and applications
par: Pathak, Ashok Kumar, et autres
Publié: (2024)

Quasi-Monte Carlo for Bayesian design of experiment problems governed by parametric PDEs
par: Kaarnioja, Vesa, et autres
Publié: (2024)

Global optimality conditions for sensor placement, with extensions to binary low-rank A-optimal designs
par: Aarset, Christian
Publié: (2024)

Berry-Esseen bounds for design-based causal inference with possibly diverging treatment levels and varying group sizes
par: Shi, Lei, et autres
Publié: (2022)

FEM-based A-optimal sensor placement for heat source inversion from final time measurement
par: Aarset, Christian, et autres
Publié: (2025)

Variational Sequential Optimal Experimental Design using Reinforcement Learning
par: Shen, Wanggang, et autres
Publié: (2023)

Multifidelity sensor placement in Bayesian state estimation problems
par: Ramon, Gabriela, et autres
Publié: (2026)

Bayesian Smoothed Quantile Regression
par: Liu, Bingqi, et autres
Publié: (2025)

Polynomial time guarantees for sampling based posterior inference in high-dimensional generalised linear models
par: Altmeyer, Randolf
Publié: (2022)

Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
par: Zhong, Shijie, et autres
Publié: (2024)

Forward selection and post-selection inference in factorial designs
par: Shi, Lei, et autres
Publié: (2023)

Nested Pseudo-GMM Estimation of Demand for Differentiated Products
par: Aguirregabiria, Victor, et autres
Publié: (2026)

Quantile Importance Sampling
par: Datta, Jyotishka, et autres
Publié: (2023)

Surrogate-Based Bayesian Inference: Uncertainty Quantification and Active Learning
par: Roberts, Andrew Gerard, et autres
Publié: (2026)

Multi-Iteration Stochastic Optimizers
par: Carlon, Andre, et autres
Publié: (2020)

A copula-based rank histogram ensemble filter
par: Subrahmanya, Amit N., et autres
Publié: (2025)

Multilevel Markov Chain Monte Carlo with likelihood scaling for Bayesian inversion with high-resolution observations
par: Vanmechelen, Pieter, et autres
Publié: (2024)

Functional Estimation of the Marginal Likelihood
par: Papaspiliopoulos, Omiros, et autres
Publié: (2026)

Some theoretical foundations for the design and analysis of randomized experiments
par: Shi, Lei, et autres
Publié: (2024)

Sampling Strategies in Bayesian Inversion: A Study of RTO and Langevin Methods
par: Laumont, Remi, et autres
Publié: (2024)

On theoretical guarantees and a blessing of dimensionality for nonconvex sampling
par: Chak, Martin
Publié: (2024)