# Definitions for conditional probabilitycon·di·tion·al prob·a·bil·i·ty

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1. conditional probability, contingent probabilitynoun

the probability that an event will occur given that one or more other events have occurred

### WikipediaRate this definition:0.0 / 0 votes

1. Conditional probability

In probability theory, conditional probability is a measure of the probability of an event occurring, given that another event (by assumption, presumption, assertion or evidence) has already occurred. This particular method relies on event B occurring with some sort of relationship with another event A. In this event, the event B can be analyzed by a conditional probability with respect to A. If the event of interest is A and the event B is known or assumed to have occurred, "the conditional probability of A given B", or "the probability of A under the condition B", is usually written as P(A|B) or occasionally PB(A). This can also be understood as the fraction of probability B that intersects with A: P ( A ∣ B ) = P ( A ∩ B ) P ( B ) {\displaystyle P(A\mid B)={\frac {P(A\cap B)}{P(B)}}} .For example, the probability that any given person has a cough on any given day may be only 5%. But if we know or assume that the person is sick, then they are much more likely to be coughing. For example, the conditional probability that someone unwell (sick) is coughing might be 75%, in which case we would have that P(Cough) = 5% and P(Cough|Sick) = 75 %. Although there is a relationship between A and B in this example, such a relationship or dependence between A and B is not necessary, nor do they have to occur simultaneously. P(A|B) may or may not be equal to P(A) (the unconditional probability of A). If P(A|B) = P(A), then events A and B are said to be independent: in such a case, knowledge about either event does not alter the likelihood of each other. P(A|B) (the conditional probability of A given B) typically differs from P(B|A). For example, if a person has dengue fever, the person might have a 90% chance of being tested as positive for the disease. In this case, what is being measured is that if event B (having dengue) has occurred, the probability of A (tested as positive) given that B occurred is 90%, simply writing P(A|B) = 90%. Alternatively, if a person is tested as positive for dengue fever, they may have only a 15% chance of actually having this rare disease due to high false positive rates. In this case, the probability of the event B (having dengue) given that the event A (testing positive) has occurred is 15% or P(B|A) = 15%. It should be apparent now that falsely equating the two probabilities can lead to various errors of reasoning, which is commonly seen through base rate fallacies. While conditional probabilities can provide extremely useful information, limited information is often supplied or at hand. Therefore, it can be useful to reverse or convert a conditional probability using Bayes' theorem: P ( A ∣ B ) = P ( B ∣ A ) P ( A ) P ( B ) {\displaystyle P(A\mid B)={{P(B\mid A)P(A)} \over {P(B)}}} . Another option is to display conditional probabilities in conditional probability table to illuminate the relationship between events.

### ChatGPTRate this definition:0.0 / 0 votes

1. conditional probability

Conditional probability is a measure of the probability of an event occurring, given that another event has already occurred. It involves the relationship between two or more events. If the event of interest is event A and event B has already occurred, the conditional probability of A given B is usually expressed as P(A|B). This can also be understood as 'the fraction of probability B that intersects with A'.

### WikidataRate this definition:0.0 / 0 votes

1. Conditional probability

In probability theory, a conditional probability is the probability that an event will occur, when another event is known to occur or to have occurred. If the events are A and B respectively, this is said to be "the probability of A given B". It is commonly denoted by P, or sometimes PB. P may or may not be equal to P, the probability of A. If they are equal, A and B are said to be independent. For example, if a coin is flipped twice, "the outcome of the second flip" is independent of "the outcome of the first flip". In the Bayesian interpretation of probability, the conditioning event is interpreted as evidence for the conditioned event. That is, P is the probability of A before accounting for evidence E, and P is the probability of A having accounted for evidence E.

### Numerology

1. Chaldean Numerology

The numerical value of conditional probability in Chaldean Numerology is: 1

2. Pythagorean Numerology

The numerical value of conditional probability in Pythagorean Numerology is: 2

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