The paper addresses the AI shutdown problem, a long-standing challenge in AI safety. The shutdown problem asks how to design AI systems that will shut down when instructed, will not try to prevent ...

This article explores the potential of large language models (LLMs) in reliability systems engineering, highlighting their ...

Quantum machine learning (QML) is an emerging research field that deals with quantum algorithms for data analysis. It is hoped that QML will yield practical demonstrations of quantum advantage by ...

Stable distributions are well-known for their desirable properties and can effectively fit data with heavy tail. However, due to the lack of an explicit probability density function and finite second ...

The mathematics that enable sensor fusion include probabilistic modeling and statistical estimation using Bayesian inference and techniques like particle filters, Kalman filters, and α-β-γ filters, ...

This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including ...

This important and elegant study makes a significant step towards harmonisation of two perspectives on synaptic plasticity in the brain: Bayesian inference and energy efficiency. Through a combination ...

Abstract: Causal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto ...