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Exact(8)
Let si be the posterior probability of observing a successful treatment predicted by classifier i.
Let be the posterior probability that set g is enriched, where D represents all observed data.
Let the true gene tree and its pseudogenization vertices be (G, ψ) and q be the posterior probability distribution.
Adapting terminology from Savage et al. (2010), we define the probability of the i-th gene being fused across datasets to be the posterior probability that For brevity, we denote this posterior probability by.
Specifically, let F be the set of Gaussian components whose parameters have already been fixed and γ ij 3 be the posterior probability that x i belongs to the jth Gaussian component.
Let Pos be the posterior probability of a TF tf m to have a specific regulatory interaction I m in regulatory module R k, where I m ∈ { AN, AS, RN, RS} (ANS and RNS will be discussed later).
Similar(51)
Nonetheless, we show that a similarity network can always answer any query of the form: "What is the posterior probability of an hypothesis given evidence?" We call this property diagnostic completeness.
The first number in every cell is the posterior probability threshold which is 1.0.
ξ(s j ) is the posterior probability of the edge phoneme s j.
In Eq. 2, P(x i |y j ) is the posterior probability of X given Y.
where the th element in is the posterior probability of according to the th component of a visual dictionary model.
More suggestions(12)
be the observed probability
becomes the posterior probability
be the prior probability
be the conditional probability
be the overall probability
be the joint probability
be the posterior surface
be the posterior wall
be the posterior mean
be the posterior shortening
be the posterior distribution
be the cumulative probability
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com