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 ...

Abstract: This study proposed a risk assessment method based on Bayesian networks for complex system engineering with multiple types of work, strong concealment, and multiple uncertain factors.

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

NetraMark Holdings Inc. (the “Company” or “NetraMark”) (CSE: AIAI) (OTCQB: AINMF) (Frankfurt: PF0) a premier artificial intelligence (AI) company that is transforming clinical trials with AI powered ...

Abstract: This paper proposes a Bayesian neural network method for predicting equipment operational trends based on a channel attention mechanism. Traditional time series prediction methods have ...

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.

The study, titled Reinforcement Learning for Monetary Policy Under Macroeconomic Uncertainty: Analyzing Tabular and Function ...

Investigators are hoping to find clues as to why the Bayesian superyacht sank off the coast of Sicily 10 months ago, killing seven people. By Emma Bubola and Jeffrey Gettleman The hull of the Bayesian ...

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 ...

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, ...