calculation of

Bayes' theorem statistics) may verify or challenge initial quick impressions but can be difficult to achieve in fast-paced clinical care settings.

By using

Bayes' theorem, one can express the probability of a document belonging to a certain class as a product of the probabilities of the words in the documents appearing in the training documents of that particular class.

In contrast, the Bayesian approach uses

Bayes' Theorem to formally combine prior information with current information on a quantity of interest.

Bayes' theorem can modify evaluations of probability based on initial assumptions in the light of more data that later becomes available.

Classification algorithms that take advantage of

Bayes' Theorem and prevalence statistics, dubbed naive Bayes classifiers, aim to accomplish this with readily available data.

Silver suggests an alternative system for probability analysis based on

Bayes' Theorem (again, I'll spare you the details), which, in very simplified language, requires taking a hard look at our assumptions, which often are based on our biases.

Bayes' Theorem states that the probability of a condition actually being present in the face of a positive result of a test is equal to the probability of the test being positive when the condition is actually present (a measure of the sensitivity of the test), multiplied by the probability of the condition actually being present, divided by the overall probability of a positive test result.

Bayes' theorem to update beliefs is widely used in many areas of applied science, engineering, economics, game theory, medicine and even law.

Bayes' theorem can be expressed in terms of the odds-ratios between two hypotheses (1):

From a subjective approach, the property of exchangeability allows to solve some controversies regarding the relationships between probability and frequency and the

Bayes' theorem, which constitutes the theoretical foundation of the learning process based on experience that allows to coherently relate assignments of probabilities for different information sets, transforming statistical inference in a particular case of inductive reasoning (5).

The methodology proposed in this article utilizes an expert judgment model within a Bayesian framework for the more complex case of continuous probability distributions, The most general form of

Bayes' Theorem applies to discrete probability distributions, and relates the conditional and prior probabilities of two events using the following equation,

This volume contains 10 chapters that review recently reported short interfering RNA (siRNA) design guidelines and clarifies the problems concerning the guidelines, as well as detailing an effective method for selecting siRNA target sequences from many possible candidate sequences using

Bayes' theorem, the development of a durable RNAi therapy for cancer and viral infections, and the structure, application, and therapeutic challenges of siRNA.

A naive Bayes classifier is a simple probabilistic classifier based on applying

Bayes' theorem with strong (naive) independence assumptions.

Under the heading "Urinalysis Data and Hair Analysis Data,"

Bayes' theorem was expressed as follows:

The BACS project is based on

Bayes' theorem, which provides a model for making rational judgements when the only information available is uncertain and incomplete.