Statistics

Improving reproducibility in machine learning requires embracing variability in results.

A new method for assessing cardiovascular disease risk based on individual factors.

Aiming to clarify Bayesian methods for astronomers with practical advice.

A new method reveals insights into human gestures and their relationship with speech.

A look at methods to assess model predictions on unseen data.

This research addresses the challenges of contaminant RNA affecting data accuracy in experiments.

An overview of voting systems that promote democracy and fair representation.

Discover a simple method for efficient data value selection.

A new approach to efficiently detect changes in various data streams.

Improve model predictions by smartly selecting input samples for Monte Carlo simulations.

Discover a simple method for efficient data value selection.

Introducing mGHS for effective network analysis across diverse datasets.

Research reveals a new approach to analyze health outcomes over time.

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.

Improving reproducibility in machine learning requires embracing variability in results.

A new model improves molecular optimization for science and medicine.

This tool improves reliability in AI predictions across various fields.

Innovative method generates storybook images from text descriptions seamlessly.

Introducing PILOT, a fast and stable algorithm for creating linear model trees.

A new model combines Gaussian processes with expert systems for better predictions.

Explore methods for sampling from complex distributions with transport maps and normalizing flows.

A look at domain generalization and its role in effective machine learning.

Introducing PILOT, a fast and stable algorithm for creating linear model trees.

A new approach improves accuracy in predicting wildfire behavior and boundaries.

A new approach to efficiently detect changes in various data streams.

A look at PCA and admixture models in understanding genetic variation and population structure.

Improve model predictions by smartly selecting input samples for Monte Carlo simulations.

New approach enhances understanding of causal relationships in research.

This article examines how Bayesian modeling aids in studying spatial data changes.

A look at methods to assess model predictions on unseen data.

Discover a simple method for efficient data value selection.

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 look at domain generalization and its role in effective machine learning.

Simplifying complex problems through surrogate models aids in quick estimations.

New insights into improving the performance of graph neural networks.

A look into IID Gaussian processes and their significance in analyzing time-dependent data.

A new method improves estimation in causal relationships with less restrictive assumptions.

Exploring DNN performance in nonparametric interaction models as dimensions increase.

Learn how trimmed means improve estimates in data analysis.

New methods aim to improve analysis of high-dimensional data using statistical techniques.