Statistics

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.

Learn how A/B testing can improve business decisions and user experiences.

New insights on the Omicron variant's impact on public health.

This article explores skill models in darts using empirical Bayes methods for player analysis.

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.

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

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

New methods reveal how speech can indicate depression severity.

JANA streamlines Bayesian modeling for faster and more accurate data analysis.

Insights on how step sizes and randomness affect neural network training methods.

Learn how active learning improves model performance with less labeled data.

A look into Federated Learning's approach to user privacy and engagement.

This work offers fresh perspectives on generative models and their performance metrics.

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.

Exploring fairness measures in risk score models across various fields.

Introducing a method for better handling complex data relationships.

This article discusses methods to improve predictions with unbalanced data in regression.

An overview of two correlation methods and a new combined approach.

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.

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.

A look into excess risk and empirical risk minimization with exp-concave losses.

A look at how Policy Mirror Descent enhances decision-making in MDPs.