conditional probability


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conditional probability

n.
The probability that an event will occur, given that one or more other events have occurred.

conditional probability

n
(Statistics) statistics the probability of one event, A, occurring given that another, B, is already known to have occurred: written P(A|B) and equal to P(A and B)|P(B)
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Noun1.conditional probability - the probability that an event will occur given that one or more other events have occurred
probability, chance - a measure of how likely it is that some event will occur; a number expressing the ratio of favorable cases to the whole number of cases possible; "the probability that an unbiased coin will fall with the head up is 0.5"
References in periodicals archive ?
This, in turn, sharpens the conditional probability of prediction errors, and increases the compression efficiency of a context adaptive entropy coder.
Its quantitative component X represented by probability tables (PT) for parent nodes and conditional probability tables (CPT) for descendant's nodes, arcs X = {[X.
So we should further clarify, second, that an action is not a probability raiser for an end simply because the conditional probability of the end given the action is higher than some relevant baseline.
An efficient and accurate method to calculate the conditional probability of failure involves an integral equation in terms of up-crossing and joint up-crossing rates [18].
The overall conditional probability for the highest ICP [P(ICP=max)] occurring during the first minute was 0.
For noncomatose stroke patients, the conditional probability of the parameters history of AF, vomiting, DM, SBP (more than 180 mmHg or not), and age (more than 65 years or not) was multiplied to obtain the actual posterior probability of CI or ICH, as appropriate.
To do this, I used discrete-time survival analysis to estimate simultaneously the conditional probability of graduation in each given year after high school entry.
It was reflected in conditional probability table (CPT) and was expressed through the following relation, PN = PN (Ni\N1, N2, .
r] between the variables; (2) a set of conditional probability distributions P = {[p.
This paper shows that, in this case, the root cause is due to improper assumptions, specifically the first hidden assumption identified by Jaynes highlighted in section 5 above, that is assuming that a conditional probability represents a physical influence instead of the physically correct logical inference.
But that bias often prevents us from calculating conditional probability correctly.
Conditional probability table of nodes, in addition to calculating a priori probability, it must also calculate the conditional probability of each node.

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