The phrase "a corresponding distribution" is correct and usable in written English.
It can be used in contexts where you are discussing a relationship between two or more distributions, often in statistics or data analysis.
Example: "In this study, we observed a corresponding distribution of responses across different demographic groups."
Alternatives: "an associated distribution" or "a related distribution".
Fluid inclusions are sensitive monitors of fluid conditions during entrapment, which record minor variation in temperature and pressure and display a corresponding distribution pattern in homogenization temperatures.
This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve a corresponding distribution of parameterized tasks/goals.
Then there is a corresponding distribution of utilities in the location of H i ( u ∗ ) = Pr ( u ( f ) ≤ u ∗ } = ∫ f ∈ L ( u ∗ ) g i ( f ) df.
A corresponding distribution of haplotype frequencies was observed when comparing PPD-negative controls with the combined groups of cases plus PPD-positive controls with (9.7% versus 15.4%; OR 2.12, CI [1.3 3.5]) and PPD-negative and PPD-positive controls (9.7% versus 15.3%; OR 2.09, CI [1.2 3.5]).
Repeated sampling produces multiple intervention sets and a corresponding distribution of objective function values.
Although this example considers a simple compartmental setup, more complex heterogeneities can be easily realized by using a corresponding distribution for the labeling efficacy.
for the action of (mathcal {G}) (i.e., (mathcal {L}) or (mathcal {M})) on (mathcal {D}_{!text{textit{textsf{textbf{x}}}}}^A) (i.e., A=E or A=S) in terms of a corresponding (distribution-function-like) "kernel" operator (text{textit{textsf{textbf{G}}}}(boldsymbol {r},boldsymbol {r}_^{prime })).
If the same number of points is used to estimate a 1D distribution and a corresponding 2D distribution, the end result will be a worse approximation for the 2D distribution, simply because of the curse of dimensionality.
In a structureless datasets, i.e., in datasets where the timing of events is statistically independent, the distribution of events per day simply follows a Poisson, the corresponding distribution of inter-event times an exponential distribution.
Fig. 10 Trajectory and velocity of gas flows (a) and their corresponding distribution of turbulence energy (b) in the eddy zone of the burner.
In this paper, we try to quantify these observations through comparisons of the voidage distribution of a fluid bed to the expected corresponding distribution of a uniform bed.
