A visualization of the Markov chain Monte Carlo algorithm, used for Bayesian
analysis, exploring parameter space.
Credit: Argonne National Laboratory/Noah Paulson
At some point in your life, you've probably had somebody—a parent, a teacher, a mentor—tell you that "the more you practice, the better you become." The expression is often attributed to Thomas Bayes, an 18th century British minister who was interested in winning at games and formalized this simple observation into a now-famous mathematical expression.
Used to examine behaviors, properties and other mechanisms that constitute a concept or phenomenon, Bayesian analysis employs an array of varied, but similar, data to statistically inform an optimal model of that concept or phenomenon.