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"for every probability" is correct and usable in written English.
You can use it when you need to express the idea that something is possible to happen in all possible cases or scenarios. For example: "We must consider for every probability, no matter how small, when making our decision."
Exact(2)
Also let be an RKHS, then k is characteristic if and only if is dense in for every probability on.
For every probability P on ({mathcal{F}}) there exists a countably additive probability (P_{c}) and a purely finitely additive probability (P_{d}) such that (P=lambda P_{c} + (1-lambda) P_{d}) for some (lambdain[0,1]).
Similar(58)
Moreover, for every threshold probability, the UEP curves always give lower MSEs than the UEP curve.
A stationary Markov process has the following property: for every joint probability P s (t 1 ) = S (1 ), s (t 2 ) = S (2 ), … and ∀ τ: (7) P s (t 1 ) = S (1 ), s (t 2 ) = S (1 ), … = P s (t 1 + τ ) = S (1 ), s (t 2 + τ ) = S (1 ), … Notice that instantaneous probabilities P s (t ) = S of a stationary stochastic process are time independent.
Suppose that we issue an alarm for every case of probability higher than the threshold.
First, we choose q∈{q 1,q 2} uniformly at random and determine for every entry the probability of being non-zero as η j =q p 1 for j≤d/2 and η j =q p 2 for j>d/2.
Figure 2A reports, for every deployment, the probability distribution of the durations of person-to-person contacts.
First, the best models for recapture probability were selected for every pond.
Then for every properly extendable qualitative probability relation ⊆ there is a probability function P such that A ⊆ B just in case P[A] ≤ P[B].
(D ) The trial-type prediction method based on firing rate was used to predict trial type from spiking activity (A, bottom) in conjunction with decoded locations as a maximum a posteriori probability for every time window (B ), and the trial-type-specific firing probability map (E ).
To estimate the expected earnings profile for each individual, we use the income distribution by age of the current patients in the sample, accounting for the probability of dying every year.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com