Join us for an exciting educational program aimed at expanding your statistical toolbox with Bayesian methods. The program is tailored towards users of statistics (but you don’t need to be a ...

A new mathematical model enhances the evaluation of AI identification risks, offering a scalable solution to balance technological benefits with privacy protection. AI tools are increasingly used to ...

The method draws from Bayesian statistics to understand how identifiable individuals are on a small scale, and extrapolate the accuracy of identification to larger populations. This makes it ...

This study is important, advancing our understanding of how humans adapt to uncertainty in dynamic environments by investigating the interplay between two types of uncertainty-volatility (systematic ...

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.

Problems with healthcare in the UK began in the era of severe financial constraints and made its economy more vulnerable to the COVID-19 ...

In a Bayesian analysis, the posterior samples from all three chains can be used to create a posterior distribution of the rankings. The summary statistics of the posterior distribution of the rankings ...

Naive Bayes classification is a popular statistical method used in machine learning for classification tasks. It is based on Bayes' theorem and assumes that the presence of a particular feature in ...

We aim to build confidence in the safety and reliability of payment service providers’ services while protecting end users from specific risks. We’re taking steps to better understand the impacts of ...

Certain classes of Bayesian hierarchical models have shown to be particularly useful in such contexts. Bayesian approaches are strongly connected to statistical computational methods, and in ...