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- Bayes' theorem is a formula that describes how to update the probabilities of hypotheses when given evidence. It follows simply from the axioms of conditional probability, but can be used to powerfully reason about a wide range of problems involving belief updates.
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2024年3月30日 · Bayes' Theorem, named after 18th-century British mathematician Thomas Bayes, is a mathematical formula for determining conditional probability. Conditional probability is the likelihood of an...
貝葉斯定理（英語： Bayes' theorem ）是概率論中的一個定理，描述在已知一些条件下，某事件的发生機率。 比如，如果已知某種健康問題与寿命有关，使用贝叶斯定理则可以通过得知某人年龄，来更加准确地计算出某人有某種健康問題的機率。
In the Bayesian (or epistemological) interpretation, probability measures a "degree of belief". Bayes' theorem links the degree of belief in a proposition before and after accounting for evidence. For example, suppose it is believed with 50% certainty that a coin is twice as likely to land heads than tails.
貝氏定理 （英語： Bayes' theorem）是 機率論 中的一個 定理，描述在已知一些條件下，某 事件 的發生機率。 比如，如果已知某種健康問題與壽命有關，使用貝氏定理則可以通過得知某人年齡，來更加準確地計算出某人有某種健康問題的機率。 通常，事件A在事件B已發生的條件下發生的機率，與事件B在事件A已發生的條件下發生的機率是不一樣的。 然而，這兩者是有確定的關係的，貝氏定理就是這種關係的陳述。 貝氏公式的一個用途，即透過已知的三個機率而推出第四個機率。 貝氏定理跟 隨機變數 的 條件機率 以及 邊際機率分布 有關。 作為一個普遍的原理，貝氏定理對於所有機率的解釋是有效的。 這一定理的主要應用為 貝氏推論，是 推論統計學 中的一種推論法。 這一定理名稱來自於 托馬斯·貝葉斯。 陳述.
Bayes' theorem is a formula that describes how to update the probabilities of hypotheses when given evidence. It follows simply from the axioms of conditional probability, but can be used to powerfully reason about a wide range of problems involving belief updates.
2024年10月24日 · Bayes’s theorem, in probability theory, a means for revising predictions in light of relevant evidence, also known as conditional probability or inverse probability. The theorem was discovered among the papers of the English Presbyterian minister and mathematician Thomas Bayes and published posthumously in 1763.
Bayes' theorem is a mathematical formula used to update the probability of a hypothesis based on new evidence. This theorem illustrates how conditional probabilities are interrelated, allowing one to revise predictions or beliefs when presented with additional data.
