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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 ...

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 ...

Statistical Science, Vol. 26, No. 2, Special Issue on Bayesian Methods That Frequentists Should Know (May 2011), pp. 162-174 (13 pages) It is argued that the Calibrated Bayesian (CB) approach to ...

Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of ...

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

When a computer scientist publishes genetics papers, you might think it would raise colleagues’ eyebrows. But Daphne Koller’s research using a once obscure branch of probability theory called Bayesian ...

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 ...

A common misconception about Bayesian statistics is that it mainly involves incorporating personal prior beliefs or subjective opinions. While priors do play a role, the core strength of Bayesian ...

This course covers the ideas underlying statistical modelling in science through the lens of causal thinking. We cover the implementation of these ideas through Bayesian computational methods and ...