This study examined the relationship between the Monetary Policy Rate (MPR) and inflation across five continents from 2014 to 2023 using both Frequentist and Bayesian Linear Mixed Models (LMM). It ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

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

“There are known knowns. There are known unknowns. But there are also unknown unknowns—things we do not yet realize we do not know.”—Donald Rumsfeld (2002) While modern machine learning (ML) ...

Abstract: Moving Horizon Estimation (MHE) is widely used for state estimation due to its ability to effectively incorporate historical information. As a single-model approach, its accuracy depends not ...

Abstract: We use Markov categories to generalize the basic theory of Markov chains and hidden Markov models to an abstract setting. This comprises characterizations of hidden Markov models in terms of ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Natural gas purification plants are a critical part of the gas ...

Key takeaway: When learning over discrete prompts used in modern vision-language models, classical PAC-Bayes bounds turn out to be remarkably tight — significantly tighter than prior PAC-Bayes results ...

Empowered by technological progress, sports teams and bookmakers strive to understand relationships between player and team activity and match outcomes. For this purpose, the probability of an event ...

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...