ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...

How do you shape perception ethically when storytelling beats facts? One compelling framework that offers clarity is Bayesian persuasion, introduced by economists Kamenica and Gentzkow in 2011, —a ...

This repository encompasses the code and report for the "B.Tech 4th year Major Project" curriculum at Indian Institute of Engineering Science and Technology, Shibpur. Cardiovascular diseases (CVDs) ...

In a world of uncertainty and shifting narratives, this post proposes a new model for investing: Bayesian edge investing. Unlike modern portfolio theory, which assumes equilibrium and perfect ...

Three crew members of the Bayesian superyacht are officially under investigation for crimes including negligence, recklessness and failing to save the ship from an oncoming storm, according to Italian ...

Bayesian methods for inference and prediction have become widespread in the social sciences (and beyond). Over the last decades, applied Bayesian modeling has evolved from a niche methodology with ...

The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, and statistics. These provide the foundation for developing the needed ...

1 Cornell Center for Astrophysics and Planetary Science (CCAPS) and Department of Statistics and Data Science, Cornell University, Ithaca, NY, United States 2 Department of Statistical Science, Duke ...

While the majority of stroke researchers use frequentist statistics to analyze and present their data, Bayesian statistics are becoming more and more prevalent in stroke research. As opposed to ...