Copertina

Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Reiser, Philipp, Aguilar, Javier Enrique, Guthke, Anneli, Bürkner, Paul-Christian
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
di: Reiser, Philipp, et al.
Pubblicazione: (2024)

Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
di: Scheurer, Stefania, et al.
Pubblicazione: (2025)

Unsupervised Continual Learning for Amortized Bayesian Inference
di: Mishra, Aayush, et al.
Pubblicazione: (2026)

Amortized Bayesian Mixture Models
di: Kucharský, Šimon, et al.
Pubblicazione: (2025)

From Mice to Trains: Amortized Bayesian Inference on Graph Data
di: Jedhoff, Svenja, et al.
Pubblicazione: (2026)

Direct Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Model
di: Faza, Ghifari Adam, et al.
Pubblicazione: (2026)

Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
di: Mishra, Aayush, et al.
Pubblicazione: (2025)

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
di: Schmitt, Marvin, et al.
Pubblicazione: (2024)

Sensitivity-Aware Amortized Bayesian Inference
di: Elsemüller, Lasse, et al.
Pubblicazione: (2023)

Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models
di: Bockting, Florence, et al.
Pubblicazione: (2023)

JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
di: Bracher, Niels, et al.
Pubblicazione: (2025)

Uncertainty Quantification via Stable Distribution Propagation
di: Petersen, Felix, et al.
Pubblicazione: (2024)

Learning Surrogate LPV State-Space Models with Uncertainty Quantification
di: Olucha, E. Javier, et al.
Pubblicazione: (2026)

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
di: Schmitt, Marvin, et al.
Pubblicazione: (2023)

Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
di: Elsemüller, Lasse, et al.
Pubblicazione: (2025)

ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification
di: Duan, Yuhan, et al.
Pubblicazione: (2025)

Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference
di: Schmitt, Marvin, et al.
Pubblicazione: (2023)

Amortized Bayesian Workflow
di: Li, Chengkun, et al.
Pubblicazione: (2024)

Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
di: Shukla, Khemraj, et al.
Pubblicazione: (2026)

Position: There Is No Free Bayesian Uncertainty Quantification
di: Melev, Ivan, et al.
Pubblicazione: (2025)

Micro-Macro Tensor Neural Surrogates for Uncertainty Quantification in Collisional Plasma
di: Chen, Wei, et al.
Pubblicazione: (2025)

Amortized Bayesian Multilevel Models
di: Habermann, Daniel, et al.
Pubblicazione: (2024)

Intuitive Joint Priors for Bayesian Linear Multilevel Models: The R2D2M2 prior
di: Aguilar, Javier Enrique, et al.
Pubblicazione: (2022)

Consistency Models for Scalable and Fast Simulation-Based Inference
di: Schmitt, Marvin, et al.
Pubblicazione: (2023)

Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference
di: Niven, Robert K., et al.
Pubblicazione: (2024)

UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration
di: Georgalis, Georgios, et al.
Pubblicazione: (2024)

Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks
di: Sheinkman, Alisa, et al.
Pubblicazione: (2025)

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
di: Song, Haoze, et al.
Pubblicazione: (2026)

Efficient Uncertainty Propagation in Bayesian Two-Step Procedures
di: Jedhoff, Svenja, et al.
Pubblicazione: (2025)

Expert-elicitation method for non-parametric joint priors using normalizing flows
di: Bockting, Florence, et al.
Pubblicazione: (2024)

Uncertainty Quantification of Data Shapley via Statistical Inference
di: Wu, Mengmeng, et al.
Pubblicazione: (2024)

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
di: Yang, Minglei, et al.
Pubblicazione: (2024)

Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition
di: Wang, Han, et al.
Pubblicazione: (2024)

NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
di: Monod, Mélodie, et al.
Pubblicazione: (2025)

Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
di: Margossian, Charles C., et al.
Pubblicazione: (2024)

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
di: Bülte, Christopher, et al.
Pubblicazione: (2025)

Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification
di: Giovanis, Dimitris G., et al.
Pubblicazione: (2024)

Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models
di: Wang, Yuanzhe, et al.
Pubblicazione: (2024)

Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantification
di: Pim, Aaron, et al.
Pubblicazione: (2025)

BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
di: Kühmichel, Lars, et al.
Pubblicazione: (2026)