marginal probability


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marginal probability

[′mär·jən·əl ‚präb·ə′bil·əd·ē]
(statistics)
Probability expressed by the two conditional probability distributions which arise from the joint distribution of two random variables.
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Equation (A16) is utilized when formulating the marginal probability density functions for f ([x.sub.2]), f ([x.sub.3]), f ([y.sub.1+2] - [y.sub.1]) and f ([y.sub.1+2+3] - [y.sub.1+2]).
This study highlights the importance of education in determining attendance in a developing country; in Mexico, the marginal probability that a high school graduate will attend a sporting event is four times that of someone with little schooling, while that of a college graduate is seven times higher.
The cleared historical data was input into the established GeNIe Bayesian network model to calculate the marginal probability and conditional probability of the Bayesian network, and the passenger security during shipping was evaluated (Figure 3).
Thus, the marginal probability density functions of cost and schedule are defined as
While the practical implications for CT scan services were somewhat less than for cardiac SSH competition, the marginal probability of .207 represents a considerable predicted reaction.
The marginal probability for GMI rating in the litigation probit regressions is 0.58%, but the marginal probability in delisting regressions is very close to zero.
For instance, we may adopt the criterion that the best label value for Yi is simply the one corresponding to the highest marginal probability obtained by summing over all other variables from the probability distribution associated with the pairwise MRF.
7) by averaging out with respect to the marginal probability element P(dw) = dw/(2[pi]) for each [theta].
The marginal probability coefficient of the first educational level (1-8 years) of decisionmaker is significant only for boiling method.
P(A) is the prior or marginal probability of A (the probability of a cocaine-positive outcome).
Notwithstanding, as Multinomial Logit coefficients do not inform about the size and direction of the effects of marginal variations in the explanatory variables over the dependent variable, we calculate, additionally, the marginal probability distribution functions corresponding to the both types of matches.
where m(x) is the marginal probability of test result x, and r(x) is the conditional disease probability given x; m(x) and r(x) clearly depend on p as well as on data contained in the ROC curve.
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