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Relevant bibliographies by topics / Aleatoric uncertainty

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Academic literature on the topic 'Aleatoric uncertainty'

Author: Grafiati

Published: 12 July 2022

Last updated: 31 July 2025

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Journal articles on the topic "Aleatoric uncertainty"

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Pamungkas, Yayi Wira. "Penggunaan Aturan Ular Tangga dalam Musik Aleatorik Berbasis Serialisme Integral." Journal of Music Science, Technology, and Industry 3, no. 2 (2020): 201–22. http://dx.doi.org/10.31091/jomsti.v3i2.1157.

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Purpose: The author does an experiment by using the rules of snake and ladder to find out and understand how the concept of uncertainty can work in serialism-based aleatoric music: by testing it using the most stringent serialism system, namely the system of integral serialism. Research methods: The process of creating the composition of this artistic research work has five stages, namely the exploration stage, the concept preparation stage, the concept analysis stage, the macro structure preparation stage, and the concept application stage. Results and discussion: The concept of snake and lad

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Berry, Lucas, and David Meger. "Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6806–14. http://dx.doi.org/10.1609/aaai.v37i6.25834.

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In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed to capture complicated aleatoric distributions. To this end, we propose an ensemble of Normalizing Flows (NF), which are state-of-the-art in modeling aleatoric uncertainty. The ensembles are created via sets of fixed dropout masks, making them less expensive than creating separate NF models. We demonstrate how to leverage the unique structure of NFs, base distributions, to estimate aleatoric uncertainty without relying on samples, provide a comprehensive set of baselines, and de

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Zhang, Wang, Ziwen Martin Ma, Subhro Das, et al. "One Step Closer to Unbiased Aleatoric Uncertainty Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16857–64. http://dx.doi.org/10.1609/aaai.v38i15.29627.

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Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties are usually categorized into aleatoric (data) and epistemic (model) uncertainty. In this paper, we point out that the existing popular variance attenuation method highly overestimates aleatoric uncertainty. To address this issue, we proposed a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer ap

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Laves, Max-Heinrich, Sontje Ihler, Jacob F. Fast, Lüder A. Kahrs, and Tobias Ortmaier. "Recalibration of Aleatoric and EpistemicRegression Uncertainty in Medical Imaging." Machine Learning for Biomedical Imaging 1, MIDL 2020 (2021): 1–26. http://dx.doi.org/10.59275/j.melba.2021-a6fd.

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The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated. We apply sigma scaling with a single scalar value; a simple, yet effective calibration method for both types of uncertainty. The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-art convolutional network ar

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Badings, Thom, Licio Romao, Alessandro Abate, and Nils Jansen. "Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14701–10. http://dx.doi.org/10.1609/aaai.v37i12.26718.

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Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. However, the underlying models exclusively capture aleatoric but not epistemic uncertainty, and thus require that model parameters are known precisely. Our contribution to overcoming this restriction is a novel abstraction-based co

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Li, Tianyi, Zhengyuan Chen, Zhen Zhang, et al. "Predicting Stress–Strain Curve with Confidence: Balance Between Data Minimization and Uncertainty Quantification by a Dual Bayesian Model." Polymers 17, no. 4 (2025): 550. https://doi.org/10.3390/polym17040550.

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Driven by polymer processing–property data, machine learning (ML) presents an efficient paradigm in predicting the stress–strain curve. However, it is generally challenged by (i) the deficiency of training data, (ii) the one-to-many issue of processing–property relationship (i.e., aleatoric uncertainty), and (iii) the unawareness of model uncertainty (i.e., epistemic uncertainty). Here, leveraging a Bayesian neural network (BNN) and a recently proposed dual-architected model for curve prediction, we introduce a dual Bayesian model that enables accurate prediction of the stress–strain curve whi

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Yu, Xuanlong, Gianni Franchi, Jindong Gu, and Emanuel Aldea. "Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 6835–43. http://dx.doi.org/10.1609/aaai.v38i7.28508.

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Uncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world applications. An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective means to estimate the uncertainty of the main task prediction without modifying the main task model. To be considered robust, an AuxUE must be capable of maintaining its performance and triggering higher uncertainties while encountering Out-of-Distribution (OOD) inputs, i.e., to provide robust aleatoric and epistemic uncertainty. However, for vision regression tasks, current AuxUE designs are mainly adopted for alea

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Meinert, Nis, Jakob Gawlikowski, and Alexander Lavin. "The Unreasonable Effectiveness of Deep Evidential Regression." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9134–42. http://dx.doi.org/10.1609/aaai.v37i8.26096.

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There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, notably with the capabilities to disentangle aleatoric and epistemic uncertainties. Despite some empirical success of Deep Evidential Regression (DER), there are important gaps in the mathematical found

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Itkina, Masha. "Perception Beyond Sensors Under Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15716–17. http://dx.doi.org/10.1609/aaai.v35i18.17855.

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My research aims to enable spatiotemporal inference in mobile robot perception systems. Specifically, the proposed thesis presents learning-based approaches to the tasks of behavior prediction and occlusion inference that explicitly account for the associated aleatoric and epistemic uncertainty.

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Mehltretter, M. "JOINT ESTIMATION OF DEPTH AND ITS UNCERTAINTY FROM STEREO IMAGES USING BAYESIAN DEEP LEARNING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 69–78. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-69-2022.

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Abstract. The necessity to identify errors in the context of image-based 3D reconstruction has motivated the development of various methods for the estimation of uncertainty associated with depth estimates in recent years. Most of these methods exclusively estimate aleatoric uncertainty, which describes stochastic effects. On the other hand, epistemic uncertainty, which accounts for simplifications or incorrect assumptions with respect to the formulated model hypothesis, is often neglected. However, to accurately quantify the uncertainty inherent in a process, it is necessary to consider all p

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Dissertations / Theses on the topic "Aleatoric uncertainty"

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Depeweg, Stefan [Verfasser], Thomas A. [Akademischer Betreuer] Runkler, Laura [Gutachter] Leal-Taixé, José Miguel [Gutachter] Hernández-Lobato, and Thomas A. [Gutachter] Runkler. "Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables / Stefan Depeweg ; Gutachter: Laura Leal-Taixé, José Miguel Hernández-Lobato, Thomas A. Runkler ; Betreuer: Thomas A. Runkler." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1199537667/34.

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Nguyen, Vu-Linh. "Imprecision in machine learning problems." Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2433.

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Nous nous sommes concentrés sur la modélisation et l'imprécision dans les problèmes d'apprentissage automatique, où les données ou connaissances disponibles souffrent d'imperfections importantes. Dans ce travail, les données imparfaites font référence à des situations où certaines caractéristiques ou les étiquettes sont imparfaitement connues, c'est-à-dire peuvent être spécifiées par des ensembles de valeurs possibles plutôt que par des valeurs précises. Les apprentissages à partir de données partielles sont couramment rencontrés dans divers domaines, tels que la biostatistique, l'agronomie ou

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Blumer, Joel David. "Cross-scale model validation with aleatory and epistemic uncertainty." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53571.

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Nearly every decision must be made with a degree of uncertainty regarding the outcome. Decision making based on modeling and simulation predictions needs to incorporate and aggregate uncertain evidence. To validate multiscale simulation models, it may be necessary to consider evidence collected at a length scale that is different from the one at which a model predicts. In addition, traditional methods of uncertainty analysis do not distinguish between two types of uncertainty: uncertainty due to inherently random inputs, and uncertainty due to lack of information about the inputs. This thesis

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Grabaskas, David. "Analysis of Transient Overpower Scenarios in Sodium Fast Reactors." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1265726176.

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Watson, Jason Daniel. "A Multi-Objective Optimization Method for Maximizing the Value of System Evolvability Under Uncertainty." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5598.

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System evolvability is vital to the longevity of large-scale complex engineered systems. The need for evolvability in complex systems is a result of their long service lives, rapid advances to their integrated technologies, unforeseen operating conditions, and emerging system requirements. In recent years, quantifiable metrics have been introduced for measuring the evolvability of complex systems based on the amount of excess capability in the system. These metrics have opened opportunities for optimization of systems with evolvability as an objective. However, there are several aspects of suc

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Sui, Liqi. "Uncertainty management in parameter identification." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2330/document.

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Afin d'obtenir des simulations plus prédictives et plus précises du comportement mécanique des structures, des modèles matériau de plus en plus complexes ont été développés. Aujourd'hui, la caractérisation des propriétés des matériaux est donc un objectif prioritaire. Elle exige des méthodes et des tests d'identification dédiés dans des conditions les plus proches possible des cas de service. Cette thèse vise à développer une méthodologie d'identification efficace pour trouver les paramètres des propriétés matériau, en tenant compte de toutes les informations disponibles. L'information utilisé

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Burgos, Simón Clara. "Advances on Uncertainty Quantification Techniques for Dynamical Systems: Theory and Modelling." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/166442.

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[ES] La cuantificación de la incertidumbre está compuesta por una serie de métodos y técnicas computacionales cuyo objetivo principal es describir la aleatoriedad presente en problemas de diversa índole. Estos métodos son de utilidad en la modelización de procesos biológicos, físicos, naturales o sociales, ya que en ellos aparecen ciertos aspectos que no pueden ser determinados de manera exacta. Por ejemplo, la tasa de contagio de una enfermedad epidemiológica o el factor de crecimiento de un volumen tumoral dependen de factores genéticos, ambientales o conductuales. Estos no siempre pueden de

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Shahtaheri, Yasaman. "A Probabilistic Decision Support System for a Performance-Based Design of Infrastructures." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/96804.

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Infrastructures are the most fundamental facilities and systems serving the society. Due to the existence of infrastructures in economic, social, and environmental contexts, all lifecycle phases of such fundamental facilities should maximize utility for the designers, occupants, and the society. With respect to the nature of the decision problem, two main types of uncertainties may exist: 1) the aleatory uncertainty associated with the nature of the built environment (i.e., the economic, social, and environmental impacts of infrastructures must be described as probabilistic); and 2) the episte

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Oskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.

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Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen gr

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Books on the topic "Aleatoric uncertainty"

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Franklin, James. Pre-history of Probability. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.3.

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The history of the evaluation of uncertain evidence before the quantification of probability in 1654 is a mass of examples relevant to current debates. They deal with matters that in general are as unquantified now as ever – the degree to which evidence supports theory, the strength and justification of inductive inferences, the weight of testimony, the combination of pieces of uncertain evidence, the price of risk, the philosophical nature of chance, and the problem of acting in case of doubt. Concepts similar to modern “proof beyond reasonable doubt” were developed especially in the legal th

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Book chapters on the topic "Aleatoric uncertainty"

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Segalman, Daniel J., and Matthew R. W. Brake. "Epistemic and Aleatoric Uncertainty in Modeling." In The Mechanics of Jointed Structures. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56818-8_33.

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Shaker, Mohammad Hossein, and Eyke Hüllermeier. "Aleatoric and Epistemic Uncertainty with Random Forests." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44584-3_35.

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Abdyssagin, Rakhat-Bi. "Heisenberg’s Uncertainty Principle and Aleatoric Technique in Music." In Quantum Mechanics and Avant-Garde Music. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63161-0_13.

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Löhr, Timo, Michael Ingrisch, and Eyke Hüllermeier. "Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification." In Artificial Intelligence in Medicine. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66535-6_17.

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Robertson, Brett A., Matthew S. Bonney, Chiara Gastaldi, and Matthew R. W. Brake. "Quantifying Epistemic and Aleatoric Uncertainty in the Ampair 600 Wind Turbine." In Dynamics of Coupled Structures, Volume 4. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15209-7_12.

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Robertson, Brett A., Matthew S. Bonney, Chiara Gastaldi, and Matthew R. W. Brake. "Quantifying Epistemic and Aleatoric Uncertainty in the Ampair 600 Wind Turbine." In The Mechanics of Jointed Structures. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56818-8_36.

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Urbina, Angel, and Sankaran Mahadevan. "Quantification of Aleatoric and Epistemic Uncertainty in Computational Models of Complex Systems." In Structural Dynamics, Volume 3. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9834-7_47.

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Kepp, Timo, Julia Andresen, Helge Sudkamp, et al. "Epistemic and Aleatoric Uncertainty Estimation for PED, Segmentation in Home OCT Images." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36932-3_7.

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Bin Mohd Nor, Ahmad Kamal, Srinivasa Rao Pedapati, Masdi Muhammad, and Mohd Amin Abdul Majid. "Demonstrating Aleatoric Uncertainty in Remaining Useful Life Prediction Using LSTM with Probabilistic Layer." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1939-8_41.

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Valiuddin, M. M. Amaan, Christiaan G. A. Viviers, Ruud J. G. van Sloun, Peter H. N. de With, and Fons van der Sommen. "Improving Aleatoric Uncertainty Quantification in Multi-annotated Medical Image Segmentation with Normalizing Flows." In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87735-4_8.

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Conference papers on the topic "Aleatoric uncertainty"

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Gao, Zixian, Xun Jiang, Xing Xu, Fumin Shen, Yujie Li, and Heng Tao Shen. "Embracing Unimodal Aleatoric Uncertainty for Robust Multimodal Fusion." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02538.

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Xiong, Ziliang, Arvi Jonnarth, Abdelrahman Eldesokey, Joakim Johnander, Bastian Wandt, and Per-Erik Forssén. "Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00351.

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De Vita, Michele, and Vasileios Belagiannis. "Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimation." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00378.

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Brinkrolf, Johannes, Valerie Vaquet, Fabian Hinder, and Barbara Hammer. "Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-156.

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Pandian, Anbumalar, Sutha Subbian, Pappa Natarajan, and Seevagan Senthilkumar Deepa. "Uncertainty Quantification of LSTM Model for SoC Estimation against Aleatoric and Epistemic Uncertainties." In 2024 Control Instrumentation System Conference (CISCON). IEEE, 2024. http://dx.doi.org/10.1109/ciscon62171.2024.10696388.

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Gao, Qinghe, Daniel C. Miedema, Yidong Zhao, Jana M. Weber, Qian Tao, and Artur M. Schweidtmann. "Bayesian uncertainty quantification of graph neural networks using stochastic gradient Hamiltonian Monte Carlo." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.111298.

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Graph neural networks (GNNs) have proven state-of-the-art performance in molecular property prediction tasks. However, a significant challenge with GNNs is the reliability of their predictions, particularly in critical domains where quantifying model confidence is essential. Therefore, assessing uncertainty in GNN predictions is crucial to improving their robustness. Existing uncertainty quantification methods, such as Deep ensembles and Monte Carlo Dropout, have been applied to GNNs with some success, but these methods are limited to approximate the full posterior distribution. In this work,

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Bloor, Maximilian, Tom Savage, Calvin Tsay, Ehecatl Antonio Del Rio Chanona, and Max Mowbray. "A Subset Selection Strategy for Gaussian Process Q-Learning of Process Optimization and Control." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.126649.

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This work addresses a practical challenge in batch process optimization: the need for sample efficient learning methods due to the high cost and time-intensive nature of running physical batch processes. While reinforcement learning (RL) offers a promising framework for optimizing batch processes, traditional approaches require numerous experimental runs to converge to optimal policies. A novel sample efficient RL method that leverages Gaussian Processes (GPs) to accelerate learning from limited batch data is proposed. However, the direct application of GPs becomes computationally intractable

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Tahir, Anique, Lu Cheng, and Huan Liu. "Fairness through Aleatoric Uncertainty." In CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management. ACM, 2023. http://dx.doi.org/10.1145/3583780.3614875.

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Segalman, Daniel J., Matthew R. Brake, Lawrence A. Bergman, Alexander F. Vakakis, and Kai Willner. "Epistemic and Aleatoric Uncertainty in Modeling." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13234.

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One major difficulty that exists in reconciling model predictions of a system with experimental measurements is assessing and accounting for the uncertainties in the system. There are several enumerated sources of uncertainty in model prediction of physical phenomena, the primary ones being: 1) Model form error, 2) Aleatoric uncertainty of model parameters, 3) Epistemic uncertainty of model parameters, and 4) Model solution error. These forms of uncertainty can have insidious consequences for modeling if not properly identified and accounted for. In particular, confusion between aleatoric and

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Liu, Jiawei, Jing Zhang, and Nick Barnes. "Modeling Aleatoric Uncertainty for Camouflaged Object Detection." In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2022. http://dx.doi.org/10.1109/wacv51458.2022.00267.

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Reports on the topic "Aleatoric uncertainty"

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Johnson, Jay Dean, Jon Craig Helton, William Louis Oberkampf, and Cedric J. Sallaberry. Representation of analysis results involving aleatory and epistemic uncertainty. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/940535.

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Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techn

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Swiler, Laura Painton, and Michael Scott Eldred. Efficient algorithms for mixed aleatory-epistemic uncertainty quantification with application to radiation-hardened electronics. Part I, algorithms and benchmark results. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/972887.

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Helton, Jon C., Dusty Marie Brooks, and Cedric Jean-Marie Sallaberry. Probability of Loss of Assured Safety in Systems with Multiple Time-Dependent Failure Modes: Incorporation of Delayed Link Failure in the Presence of Aleatory Uncertainty. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1423532.

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Unwin, Stephen D., Paul W. Eslinger, and Kenneth I. Johnson. Robustness of RISMC Insights under Alternative Aleatory/Epistemic Uncertainty Classifications: Draft Report under the Risk-Informed Safety Margin Characterization (RISMC) Pathway of the DOE Light Water Reactor Sustainability Program. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1051995.

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Abrahamson, Norman, and Zeynep Gülerce. Regionalized Ground-Motion Models for Subduction Earthquakes Based on the NGA-SUB Database. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2020. http://dx.doi.org/10.55461/ssxe9861.

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A set of global and region-specific ground-motion models (GMMs) for subduction zone earthquakes is developed based on the database compiled by the Pacific Earthquake Engineering Research Center (PEER) Next Generation Attenuation - Subduction (NGA-SUB) project. The subset of the NGA-SUB database used to develop the GMMs includes 3914 recordings from 113 subduction interface earthquakes with magnitudes varying between 5 and 9.2 and 4850 recordings from 89 intraslab events with magnitudes varying between 5 and 7.8. Recordings in the back-arc region are excluded, except for the Cascadia region. Th

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Goulet, Christine, Yousef Bozorgnia, Nicolas Kuehn, et al. NGA-East Ground-Motion Models for the U.S. Geological Survey National Seismic Hazard Maps. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2017. http://dx.doi.org/10.55461/qozj4825.

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The purpose of this report is to provide a set of ground motion models (GMMs) to be considered by the U.S. Geological Survey (USGS) for their National Seismic Hazard Maps (NSHMs) for the Central and Eastern U.S. (CEUS). These interim GMMs are adjusted and modified from a set of preliminary models developed as part of the Next Generation Attenuation for Central and Eastern North-America (CENA) project (NGA-East). The NGA-East objective was to develop a new ground-motion characterization (GMC) model for the CENA region. The GMC model consists of a set of GMMs for median and standard deviation of

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