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The probability to object to pay for these services is mostly explained by low quality/access characteristics.
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It estimates the probability of belonging to object tissue of each pixel in search area.
The idea of this contour prior model is to assign pixels (that are located inside an object contour) with a higher probability to become object.
This is so as it remains constant for each individual object, and the classification occurs by identifying the class with the largest probability to have generated the object.
The indicators of economic status also significantly impact the probability of objecting to pay for physician services.
At the initial phase, MPI assigns identical initial probabilities to all unknown objects based on Principle of Indifference [30].
Furthermore, the threshold values of the base-pairing probabilities to consider in the object function (4) depend on γ(p ).
According to the probability of moving objects (to the miners, for example) on the choice between tunnels and crossways, we propose the prediction method of path choose based on Bayesian decision, associated Nodes in tunnel and branch.
Once the saliency map is extracted (using the visual attention method), we apply a Linear Discriminant Analysis (LDA) [9] to estimate the probability of object presence.
(hat{p}left( {j|{mathbf{x}}_{new} } right)) is defined as the estimated conditional probability that object ({mathbf{x}}_{new}) belongs to the jth class given the predictor variables for that object.
The likelihood, P (V 1,…, V n | C ), gives the probability of an object which is known to belong to class C to exhibit the observed features V 1,…, V n.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com