Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

Discover how credibility theory helps actuaries use historical data to estimate risks and set insurance premiums; learn how ...

Offered through an interdisciplinary partnership, data science at CU Boulder is delivered by the Departments of Applied Mathematics, Computer Science, and Information Science and awarded by the ...

For humans and machines, intelligence requires making sense of the world — inferring simple explanations for the mishmosh of information coming in through our senses, discovering regularities and ...

Scientists have developed a method to identify symmetries in multi-dimensional data using Bayesian statistical techniques. Bayesian statistics has been in the spotlight in recent years due to ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...

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 ...