Bayesian statistics represents a powerful framework for data analysis that centres on Bayes’ theorem, enabling researchers to update existing beliefs with incoming evidence. By combining prior ...

Statistical inference with complex survey data is challenging because the sampling design can be informative, and ignoring it can produce misleading results. Current methods of Bayesian inference ...

The lack of an agreed inferential basis for statistics makes life "interesting" for academic statisticians, but at the price of negative implications for the status of statistics in industry, science, ...

Stochastic dynamical systems arise in many scientific fields, such as asset prices in financial markets, neural activity in ...

Here’s our estimate of public support for vouchers, broken down by religion/ethnicity, income, and state: (Click on image to see larger version.) We’re mapping estimates from a hierarchical Bayes ...

Everyone who spends time with children knows how incredibly much they learn. But how can babies and young children possibly learn so much so quickly? In a recent article in Science, I describe a ...

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...

WILMINGTON, N.C. & COLLEGE STATION, Texas--(BUSINESS WIRE)-- PPD, Inc. (Nasdaq: PPDI) and Berry Consultants, LLC today announced they have entered into a collaboration in the area of Bayesian ...