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Note that as discussed earlier, the joint exposure and adoption parameter (JEA) is consistently estimated by the sample average of the observed adoption outcome values: ( Jwidehat{E}A=frac{1}{n}{displaystyle sum_{i=1}^n{y}_i} ).
These scores were then used to construct an acceptable health curve (AHCAGGREGATE), defined by the sample average acceptable quality of life score at each age.
The expected uncovered demand f2 is then approximated by the sample average f ¯ 2 = 1 N ∑ ν = 1 N R (y, z, ξ.
Replacing μ by the sample average x creates a bias in variance estimation, which in the unweighted case is corrected by a factor n/(n - 1), where n is the number of replicates.
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Finally the problem was solved by applying the sample average approximation algorithm.
We use Conditional Value at Risk (CVaR) to approximate the chance constraint, and we get the approximation problem of the chance constrained stochastic programming by using the sample average approximation (SAA) method.
We can see that by adding τ>0 and applying the sample average approximation method, the new strategy set is still feasible with large probability when the sample number L is sufficiently large.
The sample average, represented by the one class model, showed the highest probability of endorsement to be 20% for financial difficulties, paternal atypical parenting and family discord (mild, moderate and severe combined).
Secondly, individual utilities are adjusted to an optimal mix of average sample utilities and individual utilities, based on the amount of information provided by participants and the variance in the sample average (Markov Chain Monte Carlo).
The SRW is biased towards higher degree nodes and by Theorem 1, the sample averages converge to the stationary average.
By the law of large numbers, the sample averages converge in probability and almost surely to the expected value as the sample number tends to infinity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com