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One of the many applications of Bayes’ theorem is Bayesian inference, a particular approach to statistical inference.When applied, the probabilities involved in Bayes’ theorem may have different probability interpretations. Selector .selector_input_interaction .selector_input. Selector .selector_input_interaction .selector_spinner.

In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". In the table, the values w, x, y and z give the relative weights of each corresponding condition and case.

The figures denote the cells of the table involved in each metric, the probability being the fraction of each figure that is shaded. P(A|B) = Bayesian inference derives the posterior probability as a consequence of two antecedents, a prior probability and a "likelihood function" derived from a statistical model for the observed data.

more Figs are keystone resources that sustain chimpanzees when preferred fruits are scarce.

Many figs retain a green(ish) colour throughout development, a pattern that causes chimpanzees to evaluate edibility on the basis of achromatic acces- sory cues.

To explore this concept, we report on the foraging behaviours of chimpanzees and the spectral, chemical and mechanical properties of figs, with cutting tests revealing ease of fracture in the mouth.

By integrating the ability of different sensory cues to predict fructose content in a Bayesian updating framework, we quantified the amount of information gained when a chimpanzee succes- sively observes, palpates and bites the green figs of Ficus sansibarica.

Bayesian updating is particularly important in the dynamic analysis of a sequence of data.

Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law.

On your way to the hotel you discover that the National Basketball Player's Association is having a convention in town and the official hotel is the one where you are to stay, and furthermore, they have reserved all the rooms but yours.

The whole idea is to consider the joint probability of both events, A and B, happening together (a man over 5'10" who plays in the NBA), and then perform some arithmetic on that relationship to provide a updated (posterior) estimate of a prior probability statement.

It was further developed by Pierre-Simon Laplace, who first published the modern formulation in his 1812 “Théorie analytique des probabilités.” Sir Harold Jeffreys put Bayes’ algorithm and Laplace's formulation on an axiomatic basis.