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Teorema de Bayes

1763-12-23
  • Thomas Bayes
  • Pierre-Simon Laplace
Historical study room with mathematician calculating Bayes' theorem.

(Imagem gerada apenas para fins ilustrativos)

Bayes’ theorem describes the probability of an event based on prior knowledge of conditions that might be related to the event. It is a fundamental concept in probability theory and statistics. Mathematically, it is stated as [latex]P(A|B) = \frac{P(B|A)P(A)}{P(B)}[/latex], where A and B are events and [latex]P(B) \neq 0[/latex]. It relates the conditional and marginal probabilities of two random events.

Bayes’ Theorem provides a mathematical way to update existing beliefs with new evidence. In the formula [latex]P(A|B) = \frac{P(B|A)P(A)}{P(B)}[/latex], [latex]P(A|B)[/latex] is the posterior probability: the probability of hypothesis A given the evidence B. [latex]P(B|A)[/latex] is the likelihood: the probability of observing evidence B if hypothesis A is true. [latex]P(A)[/latex] is the prior probability: the initial belief in hypothesis A before seeing evidence B. Finally, [latex]P(B)[/latex] is the marginal likelihood or evidence: the total probability of observing the evidence B under all possible hypotheses. This term serves as a normalization constant, ensuring the posterior probabilities sum to one.

The theorem was first presented in Thomas Bayes’s essay “An Essay towards solving a Problem in the Doctrine of Chances,” which was read to the Royal Society in 1763 after his death. However, it was Pierre-Simon Laplace who independently developed and popularized the theorem, applying it to problems in celestial mechanics, medical statistics, and jurisprudence. The novelty of the theorem was its formalization of inverse probability—reasoning from effects back to their causes. While classical (frequentist) statistics focuses on the probability of data given a hypothesis, Bayesian statistics focuses on the probability of the hypothesis given the data, which is often a more intuitive and direct answer to scientific questions.

UNESCO Nomenclature: 1208
Estatísticas

Tipo

Sistema abstrato

Interrupção

Revolucionário

Uso

Uso generalizado

Precursores

  • General theory of probability (developed by Pascal, Fermat, Bernoulli)
  • Conceito de probabilidade condicional
  • Trabalhos sobre problemas de probabilidade inversa realizados por matemáticos anteriores.

Aplicações

  • Filtragem de spam em clientes de e-mail
  • Testes de diagnóstico médico
  • Algoritmos de aprendizado de máquina (por exemplo, classificadores Naive Bayes)
  • Operações de busca e salvamento
  • Modelagem ecológica
  • Previsão do mercado financeiro

Patentes:

NA

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Relacionado a: teorema de Bayes, probabilidade condicional, posterior, prior, verossimilhança, estatística bayesiana, probabilidade inversa, Thomas Bayes, Laplace, evidência.

Contexto histórico

Teorema de Bayes

1650
1736
1750
1763-12-23
1780
1805
1822
1640
1650
1747
1758
1777
1799
1812
1822

(Caso a data seja desconhecida ou irrelevante, por exemplo, "mecânica dos fluidos", é fornecida uma estimativa aproximada de seu surgimento notável)

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