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This information allows calculating the probability of inclusion in the sample at the selected locations.
We propagate this uncertainty to obtain the probability of inclusion of each reaction in the simplified mechanism.
In addition to the previously introduced notation, π ij is the joint probability of inclusion for unit i and j.
This is done using some sampling design such that the probability of inclusion of any sampling unit from the population is known.
The CS methodology provides a scheme to estimate a set of weights to correct the initial probability of inclusion in the sample (Fasani, 2008; Kraler & Vogel, 2008; Vogel & Kovacheva, 2008).
The application of the snowball method, one of the most traditional non-random methodologies, presents some difficulties in determining the initial list and thus risks introducing significant biases, and the probability of inclusion in the sample is unknown.
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This is done by considering the probability of inclusion-matrix debonding and the fatigue crack initiation potency of partially-debonded inclusions for a given load ratio and stress amplitude.
The BMA analysis yields the posterior probabilities of inclusion (PIPs) and the sign certainty index of a relationship.
In population studies, it is standard to sample data via designs in which the population is divided into strata, with the different strata assigned different probabilities of inclusion.
Another type of distortion is induced by the adoption of a sampling scheme that assigns to the units in the original distribution unequal probabilities of inclusion in the sample.
For example, in studies involving complex sampling methods where units have differential probabilities of inclusion, propensity score weighting may be particularly recommended [3].
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