Models with intractable normalizing functions arise frequently in statistics. Common examples of such models include exponential random graph models for social networks and Markov point processes for ...

Bayesian statistics has emerged as a powerful methodology for making decisions from data in the applied sciences. Bayesian brings a new way of thinking to statistics, in how it deals with probability, ...

Procedures of statistical inference are described which generalize Bayesian inference in specific ways. Probability is used in such a way that in general only bounds may be placed on the probabilities ...

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 ...

Bayesian inference grew out of Bayes' theorem, a mathematical result from English clergyman Thomas Bayes, published two years after his death in 1761.

Bayes' theorem is a formula for calculating the probability of an event. Learn how to calculate Bayes' theorem and see examples.