Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...

Learn how prior probability informs economic theory and decision-making in Bayesian statistics. Understand its role before collecting new data.

An interdisciplinary team of University of Tennessee, Knoxville researchers recently published in Biophysical Journal on ...

What’s often misunderstood about Google’s incrementality testing and how Bayesian models use probability to guide better ...

Here are 11 free NPTEL data science and analytics courses from leading IITs cover graph theory, Bayesian modelling, Python, R ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

If you’d like an LLM to act more like a partner than a tool, Databot is an experimental alternative to querychat that also works in both R and Python. Databot is designed to analyze data you’ve ...

A year ago today, a violent storm struck the coast of the sleepy Sicilian fishing village of Porticello. High winds and dramatic thunder and lightning are not unheard of around this time of year in ...

Introduction: While imagery practice is effective for performance enhancement, its impact on mental health is inconclusive due to mixed findings and heterogeneous athlete populations. This study aims ...

Have you ever found yourself wrestling with Excel formulas, wishing for a more powerful tool to handle your data? Or maybe you’ve heard the buzz about Python in Excel and wondered if it’s truly the ...