Statistics

Explore effective strategies for managing cyber vulnerabilities and risks in organizations.

Research focuses on modeling xenon diffusion in uranium dioxide for improved reactor performance.

A new method enhances verbal autopsy efficiency and accuracy.

A new model helps forecast hemoglobin levels after ICU discharge.

A new method enhances our understanding of Asiatic black bear distribution.

Exploring fairness measures in risk score models across various fields.

Exploring how graphical models reveal disease spread patterns over time.

Examining how cryptocurrencies like Bitcoin and Ethereum relate to traditional financial markets.

This article examines the trade-offs in factorized approximations used in variational inference.

SNAMs offer clearer insights into machine learning predictions.

A new method improves data classification using covariance matrix clustering.

Learn about key concepts in optimization and sampling for effective decision-making.

Discover innovative methods for analyzing longitudinal data using the lqmix R package.

RESI streamlines the reporting of effect sizes in research studies.

A focused approach to improving variational inference accuracy.

This study highlights methods for analyzing brain signals using covariance matrices.

Learn how prediction sets help manage uncertainty in changing environments.

A new method improves anomaly detection in changing data environments.

Exploring the use of ARNNs in understanding physical systems and particle interactions.

New neural networks tackle complex data using advanced manifold techniques.

Examining vulnerabilities in machine learning models accessed through APIs.

Learn how personalized prediction improves accuracy across different domains.

An overview of how cvnn utilize complex numbers for advanced data modeling.

A new kernel method enhances tensor data classification in healthcare applications.

Learn how prediction sets help manage uncertainty in changing environments.

Explore effective strategies for managing cyber vulnerabilities and risks in organizations.

New neural networks tackle complex data using advanced manifold techniques.

Learn how change-point detection identifies shifts in processes across different fields.

Introducing the Graphical Dirichlet Process for effective clustering of non-exchangeable groups.

A survey reveals the struggle for reliable electricity in rural and peri-urban Nigeria.

Active learning tackles the challenge of labeling data efficiently in machine learning.

Bayesian Pseudo-Label Selection enhances model training using limited labeled data.

New sampling methods improve efficiency and accuracy in Bayesian Neural Networks.

Examining the reliability and validation of influence functions in deep learning models.

A new algorithm enhances accuracy in forecasting disease outbreaks through detailed data generation.

A discussion on merging statistical approaches for better data analysis.

A fresh approach uncovers individual variations in program treatment effects.

Methods to accurately calculate averages and variances despite missing data points.

A method for predicting specific outcomes using relevant past data.

A new framework for better statistical inference in research and analysis.

Learn how prediction sets help manage uncertainty in changing environments.

This paper introduces cardinality sparsity to improve data representation and computation.

A new method improves uniformity testing for high-dimensional data distributions.

A look into GLMs and their relationship with Gaussian mixtures.

This research investigates how Gaussian mixtures can enhance data modeling techniques.

This article highlights the role of non-separable kernels in Gaussian processes.

iPCA offers a reliable method for analyzing grouped data relationships.

Explore methods for reliable predictions in machine learning through confidence sets.