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This paper addresses the influence of the cM uncertainty on the predicted land settlement using a stochastic simulation approach where cM is regarded as a random variable and a large number of equally likely cM realizations are generated and implemented into a poroelastic finite element model.
Each SPN was treated as a random variable, and a Markov random field approach was employed to iteratively assign a class label to each SPN.
During this process, each SPN is treated as a random variable and a Markov random field (MRF) approach is employed to iteratively assign a class label to each SPN (i.e., random variable).
Indeed, since the next 10 year study will include all deaths from last 7 years of entrants with a maximum 7 year follow-up that are included in the current one, let's say, 250 deaths, only about 150 or so "new" deaths may have to be treated as a random variable, and a more reasonable final estimate might look like 250+150(1+/−1.96/(150)1/2) = 400+/−24 deaths.
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This is analogous to random effects estimation in that it treats between-subject variance as a random variable, while fitting within-subject parameters simultaneously.
During analysis, patient data were fitted as a random variable with the time as the fixed variable.
Future studies may incorporate time to awakening as a random variable structured as a Markov process.
Population was treated as a random variable.
Patient was used as a random variable.
We considered the experimenter as a random variable.
The litter was used as a random variable.
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