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Then, we can predict (tilde {y}) using the conditional distribution.
If instead we calculate the probability of being in a certain cell by using the conditional distribution of in-programme duration, i.e. conditional on being in a certain quantile of the pre-programme duration, we obtain weights to the nine different combinations of durations for each programme which are different from 1.
The BCMI produces the clean speech estimate (hat {mathbf {x}}_{mathrm {u}}) using the conditional distribution p(x u∣x r) with an assumption that the observed noisy speech x u acts as the upper bound for the underlying clean speech.
Moreover, we proposed a mid- p confidence interval using the conditional distribution of (M,S) given m = 2.
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To construct an estimator that would be both conditionally and unconditionally unbiased, one could also derive an estimator for trials stopping at the first stage that would use the conditional distribution given X1≤ r1.
The conditional expected power averages the traditional frequentist power curve using the conditional prior distribution of the true unknown treatment effect θ as the averaging weight.
Compared to the traditional gauge adjusted parametric relationship between reflectivity (Z) and ground rainfall (R), the suggested new approach is based on a nonparametric radar rainfall estimation method (NPR) derived using the conditional probability distribution of reflectivity and gauge rainfall.
The sampling of LSM parameter γ rj ( t + 1 ) is performed by using the conditional posterior distribution which is derived by combining a likelihood function of λ r j and an exponential prior density of γ r j with parameter λ γ rj.
The sampling of the other LSM parameter δ rj ( t + 1 ) is performed by using the conditional posterior distribution which is derived from a likelihood function of λ r j and a gamma prior density of δ r j with parameters { α δ rj, β δ rj } p ( δ rj | λ rj ( t + 1 ), γ rj ( t + 1 ) ) ∝ ( δ rj ) γ rj ( t + 1 ) exp { − δ rj λ rj ( t + 1 ) } G ( δ rj | α δ rj, β δ rj ).
Here we use a hybrid method where the time evolution of the marginal PDF for mixture fraction is solved and a presumed β-PDF is used for the conditional distribution of the normalized sensible enthalpy.
List of target earthquakes used for the conditional distribution.
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