Cover Image

Parsimonious Generative Machine Learning for Non-Gaussian Tail Modeling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yan, Xing, Zhao, Yue, Wu, Qi, Ma, Wenxuan
Format:	Preprint
Published:	2024
Subjects:	Applications
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles
by: Szwagier, Tom, et al.
Published: (2025)

A Data-Parsimonious Model for Long-Term Risk Assessments of West Nile Virus Spillover
by: Hosseini, Saman, et al.
Published: (2025)

Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions
by: Ibrahim, Shibal, et al.
Published: (2021)

Scalable Gaussian Process for Learning Non-Ergodic Ground Motion Model from Physics-Based Simulations with Application to Power Infrastructure Assessment
by: Zhao, Jinyan, et al.
Published: (2026)

Crop Yield Time-Series Data Prediction Based on Multiple Hybrid Machine Learning Models
by: Yan, Yueru, et al.
Published: (2025)

Data-Efficient Generative Modeling of Non-Gaussian Global Climate Fields via Scalable Composite Transformations
by: Brachem, Johannes, et al.
Published: (2026)

Bayesian Image-on-Image Regression via Deep Kernel Learning based Gaussian Processes
by: Ma, Guoxuan, et al.
Published: (2023)

Source Number Estimation via Entropy Estimation of Eigenvalues (EEE) in Gaussian and Non-Gaussian Noise
by: Asadi, Hamid, et al.
Published: (2013)

Multi-output Extreme Spatial Model for Complex Aircraft Production Systems
by: Lee, Cheolhei, et al.
Published: (2026)

A Non-parametric Approach to Inference about the Tail of a Continuous or a Discrete Distribution
by: Zhang, Jialin, et al.
Published: (2022)

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
by: Montreuil, Yannis, et al.
Published: (2026)

Confidence Scoring for LLM-Generated SQL in Supply Chain Data Extraction
by: Ma, Jiekai, et al.
Published: (2025)

Quantifying Very Extreme Precipitation and Temperature Using Huge Ensembles Generated by Machine Learning-based Climate Model Emulators
by: Paciorek, Christopher J., et al.
Published: (2025)

Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes
by: Ladopoulou, Domniki, et al.
Published: (2025)

Comparative Evaluation of Machine Learning Models for Predicting Donor Kidney Discard
by: Schliephacke, Peer, et al.
Published: (2026)

Combined Quantile Forecasting for High-Dimensional Non-Gaussian Data
by: Park, Seeun, et al.
Published: (2024)

Research Funding as a Decision Problem Under Heavy-Tailed Uncertainty
by: Sorzano, Carlos Oscar S., et al.
Published: (2026)

Exact Methods of Homogeneity Test of Proportions for Bilateral and Unilateral Correlated Data
by: Liang, Shuyi, et al.
Published: (2023)

Efficient Long-Term Structural Reliability Estimation with Non-Gaussian Stochastic Models: A Design of Experiments Approach
by: Winter, Sebastian, et al.
Published: (2025)

Scenario Analysis with Multivariate Bayesian Machine Learning Models
by: Pfarrhofer, Michael, et al.
Published: (2025)

Prediction of the Economic Behavior of Fishery Biotechnology Companies Based on Machine Learning-Based Deep Metacellular Automata
by: Chen, Liguo, et al.
Published: (2024)

Estimations of the Local Conditional Tail Average Treatment Effect
by: Chen, Le-Yu, et al.
Published: (2021)

Cauchy-Gaussian Overbound for Heavy-tailed GNSS Measurement Errors
by: Li, Zhengdao, et al.
Published: (2026)

A Non-stationary, Amortized, Transfer Learning Approach for Modeling Italian Air Quality
by: Moro, Alessandro Fusta, et al.
Published: (2026)

Categorising the World into Local Climate Zones -- Towards Quantifying Labelling Uncertainty for Machine Learning Models
by: Hechinger, Katharina, et al.
Published: (2023)

Dispersion based Recurrent Neural Network Model for Methane Monitoring in Albertan Tailings Ponds
by: Saha, Esha, et al.
Published: (2024)

Forecasting Arctic Temperatures With Quantile Machine Learning
by: Berk, Richard
Published: (2025)

MOOSE ProbML: Parallelized Probabilistic Machine Learning and Uncertainty Quantification for Computational Energy Applications
by: Dhulipala, Somayajulu L. N., et al.
Published: (2025)

Tail-adaptive Bayesian shrinkage
by: Lee, Se Yoon, et al.
Published: (2020)

Trend-encoded Probabilistic Multi-order Model: A Non-Machine Learning Approach for Enhanced Stock Market Forecasts
by: Wang, Peiwan, et al.
Published: (2025)

Modelling the error structure in Urban Building Energy Models with a Gaussian Processbased approach
by: Wang, Chunxiao, et al.
Published: (2025)

Second-order Asymptotic Analysis of Tail Probabilities of Randomly Weighted Sums: With Applications to a Bidimensional Discrete-time Risk Model
by: Geng, Bingzhen, et al.
Published: (2025)

The CUSUM Test with Observation-Adjusted Control Limits in Parameters Change Detection for the Extremely Heavy-Tailed Distributions Sequences
by: Tang, F., et al.
Published: (2024)

Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability
by: Chen, Shuheng, et al.
Published: (2025)

A Comparison of Full Information Maximum Likelihood and Machine Learning Missing Data Analytical Methods in Growth Curve Modeling
by: Tang, Dandan, et al.
Published: (2023)

Machine Learning of Public Sentiments toward Wind Energy in Norway
by: Vågerö, Oskar, et al.
Published: (2023)

Time-Varying Home Field Advantage in Football: Learning from a Non-Stationary Causal Process
by: Qi, Minhao, et al.
Published: (2025)

Gaussian Process Modeling with Genotype x Environment Kernels for Wheat Performance Prediction
by: Friedli, Lea, et al.
Published: (2025)

Bayesian Estimation of Extreme Quantiles and the Exceedance Distribution for Paretian Tails
by: Johnston, Douglas E.
Published: (2025)

Approximating Heavy-Tailed Distributions with a Mixture of Bernstein Phase-Type and Hyperexponential Models
by: Ziani, Abdelhakim, et al.
Published: (2025)