Résumé
In probability theory, the complement of any event A is the event [not A], i.e. the event that A does not occur. The event A and its complement [not A] are mutually exclusive and exhaustive. Generally, there is only one event B such that A and B are both mutually exclusive and exhaustive; that event is the complement of A. The complement of an event A is usually denoted as A′, Ac, A or . Given an event, the event and its complementary event define a Bernoulli trial: did the event occur or not? For example, if a typical coin is tossed and one assumes that it cannot land on its edge, then it can either land showing "heads" or "tails." Because these two outcomes are mutually exclusive (i.e. the coin cannot simultaneously show both heads and tails) and collectively exhaustive (i.e. there are no other possible outcomes not represented between these two), they are therefore each other's complements. This means that [heads] is logically equivalent to [not tails], and [tails] is equivalent to [not heads]. In a random experiment, the probabilities of all possible events (the sample space) must total to 1— that is, some outcome must occur on every trial. For two events to be complements, they must be collectively exhaustive, together filling the entire sample space. Therefore, the probability of an event's complement must be unity minus the probability of the event. That is, for an event A, Equivalently, the probabilities of an event and its complement must always total to 1. This does not, however, mean that any two events whose probabilities total to 1 are each other's complements; complementary events must also fulfill the condition of mutual exclusivity. Suppose one throws an ordinary six-sided die eight times. What is the probability that one sees a "1" at least once? It may be tempting to say that Pr(["1" on 1st trial] or ["1" on second trial] or ... or ["1" on 8th trial]) = Pr("1" on 1st trial) + Pr("1" on second trial) + ... + P("1" on 8th trial) = 1/6 + 1/6 + ... + 1/6 = 8/6 = 1.3333...
À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Séances de cours associées (7)
Probabilité avancée: théorème de Bayes et variables aléatoires
Couvre les concepts avancés de probabilité, y compris le théorème de Bayes et les variables aléatoires.
Théorie de la probabilité: propriétés et principes combinatoires
Explore les propriétés de probabilité, le principe d'inclusion-exclusion, les règles combinatoires, et le calcul de probabilité de coïncidence d'anniversaire.
Probabilité et statistiques
Introduit des concepts clés en probabilité et en statistiques, tels que les événements, les diagrammes de Venn et la probabilité conditionnelle.
Afficher plus