Sentence examples for denotes a subset of from inspiring English sources
where x s denotes a subset of the variables, and factor f s is a function of a corresponding set of variables x s.
The overlap rate of two point sets P and Q is defined as o ( P, Q ) = min | P ∈ Q | | P |, | Q ∈ P | | Q |, where | · | denotes the size of a point set and P ∈ Q denotes a subset of P in which for each point there exist at least one point in Q whose distance to it is below a given threshold.
Let (mathcal {M}_{1}) denotes a subset of predictors dictating h 1,k, k=0,1,2, and let (widehat {theta }_{1}^{(ell)}(mathcal {M}_{1}, mathcal {M}_{2})) denote the coefficients estimated in step (Q3) of the Q-learning algorithm using predictors (mathcal {M}_{1}) and predicted outcomes (widehat {Y}_{i}^{(ell)}(mathcal {M}_{2}),i=11,ldots, n).
where P ∠ Z k denotes a partition subset P of the measurement set Z k, W denotes a subset of a partition P, and ∪ W ∈ PW = Z k. ϕ z (x) denotes the measurement likelihood function of one measurement originating from an extended target x, r(x) is the measurement expectation, and p D (x) denotes the detection probability of the sensor.
The likelihood is calculated by observing a previous snapshot (denotes a subset of the graph after removing the edges used for performance testing purposes) of the graph and getting an estimation of the dependency of the probability of the existence of a link in the target layer given a predictor layer.
■ v denotes a subset of the i strategies including vaccination (2 strategies out of 4, 1 with vaccination alone and 1 with both vaccination and screening).
Philosophical concepts do not carve out a segment of reality but rather provide a way of describing it: in its philosophical employment, the concept of action does not distinctively denote a subset of objects, i.e., the deeds performed by animals of the human species, but is rather a way of describing what happens as an expression of rational as opposed to causal processes.
Let (mathcal {M}_{2}) denote a subset of predictors dictating the features h 2,k, k=0,1, and let (widehat {theta }_{2}^{(ell)}(mathcal {M}_{2})) denote the coefficients obtained by applying step (Q1) of the Q-learning algorithm with predictors (mathcal {M}_{2}) to the ℓth imputed dataset.
Let (mathcal{S}) denote a subset of the θ clinics and let S denote the number of clinics in this subset, then given a set of clinics (mathfrak{C}={ k_{1}, k_{2},ldots, k_{theta } } ) that form the newly created cluster, a set of transports has to visit the θ clinics, starting and finishing in the pivot node (shovel node), with as objective to minimize the total travel time.
Similar letters denote a subset of distance categories whose column proportions do not differ significantly from each other at the 0.05 level.
Each k-mer in L is considered one at a time in that order starting with L[ 1].We use LN[ i] to denote a subset of top i k-mers in L whose overall HES is maximized over all possible such subsets that include the ith k-mer, where 1 ≤ i ≤ min{10, | L|}.
