This article defines some terms which characterize probability distributions of two or more variables.

Conditional probability is the probability of some event A, given the occurrence of some other event B. Conditional probability is written P(A|B), and is read "the probability of A, given B".

Joint probability is the probability of two events in conjunction. That is, it is the probability of both events together. The joint probability of A and B is written $P(A\; \backslash cap\; B)$ or $P(A,\; B).$

Marginal probability is the probability of one event, regardless of the other event. Marginal probability is obtained by summing (or integrating, more generally) the joint probability over the unrequired event. This is called marginalization. The marginal probability of A is written P(A), and the marginal probability of B is written P(B).

In these definitions, note that there need not be a causal or temporal relation between A and B. A may precede B, or vice versa, or they may happen at the same time. A may cause B, or vice versa, or they may have no causal relation at all.

Conditioning of probabilities, i.e. updating them to take account of (possibly new) information, may be achieved through Bayes' theorem.

Definition

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If A and B are events, and P(B) > 0, then the conditional probability of A, given B is

$P(A\; \backslash mid\; B)=\; \backslash frac\{P(A\; \backslash cap\; B)\}\{P(B)\}.$

Equivalently, we have

$P(A\; \backslash cap\; B)=P(A\backslash mid\; B)\; P(B).$

Statistical independence

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Two random events A and B are statistically independent if and only if

$P(A\; \backslash cap\; B)\; \backslash \; =\; \backslash \; P(A)\; P(B).$

Thus, if A and B are independent, then their joint probability can be expressed as a simple product of their individual probabilities.

Equivalently, for two independent events A and B,

$P(A|B)\; \backslash \; =\; \backslash \; P(A)$

and

$P(B|A)\; \backslash \; =\; \backslash \; P(B).$

In other words, if A and B are independent, then the conditional probability of A, given B is simply the individual probability of A alone; likewise, the probability of B given A is simply the probability of B alone.

Mutual exclusivity

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Two events A and B are mutually exclusive if and only if

$P(A\; \backslash cap\; B)\; =\; 0$

as long as

$P(A)\; \backslash ne\; 0$

and

$P(B)\; \backslash ne\; 0.$

Then

$P(A\backslash mid\; B)\; =\; 0$

and

$P(B\backslash mid\; A)\; =\; 0.$

In other words, the probability of A happening, given that B happens, is nil since A and B cannot both happen in the same situation; likewise, the probability of B happening, given that A happens, is also nil.

Other considerations

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• If $B$ is an event and $P(B)\; >\; 0$, then the function $Q$ defined by $Q(A)\; =\; P(A|B)$ for all events $A$ is a probability measure.

• If $P(B)=0$, then $P(A|B)$ is left undefined.

• Conditional probability can be calculated with a decision tree.

See also

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• Bayes' theorem
• Likelihood function
• Posterior probability
• Probability theory
• Monty Hall problem

Probability theory

Bedingte Wahrscheinlichkeit | Probabilidad condicionada | Vikipedio:Projekto matematiko/Kondiĉa probablo | Probabilité conditionnelle | Probabilità condizionata | Voorwaardelijke kans | Prawdopodobieństwo warunkowe | Условная вероятность | Conditional probability | Betingad sannolikhet | Xác suất có điều kiện | Умовна ймовірність