"Bayes is a very limited special case of the replicator equation"

"I think the predominant philosophy [in the mid of the 21st century] will be a Bayes/non-Bayes synthesis or compromise, and that the Bayesian part will be mostly hierarchical." -- I. J. Good, 1980!

Positively correlated variables are all correlated with a linear combination of each other, and this becomes no more profound when the variables are scores on intelligence tests.

"It should in theory be doable by someone with just a good introductory undergraduate course in statistics, including multiple regression."

Presents a series of objections to Bayesian inference, written in the voice of a hypothetical anti-Bayesian statistician.

Automating science, step n: Provide an entropy-based measure of usefulness for proposed experimental programs before they are performed.

On nonlinear filtering, cellular automata and coherent structures.

Peter Norvig shows how to achieve 80 to 90% accuracy for English spelling correction in 20 lines of python code.

This is a summary for the collected results of the SL4 readership survey carried out in September 2006.

Solomonoff induction is a mathematically rigorous, idealized form of induction, that is, predicting what will happen in the future based on prior experiences.

The table below was prepared in response to frequent inquiries, especially from the media, asking questions such as, "What are the odds of being killed by lightning?" or "What are the chances of dying in a plane crash?"

A naive Bayes classifier (also known as Idiot's Bayes) is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions.

Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true.