I then defined a couple of simple parameters to derive a measure of customer satisfaction.
For each item (i in I), we then define the set of the top-N most similar items to i as (L_{i, N}).
The strength s i of node i is then defined [3] as the total number of calls where i participates, and the degree k i as usual as the number of links that node i has.
The set of all alignments of pe i is then defined as Align (pe i )={ a1 pe i, a2 pe i, …, a j pe i }.
For each PPI network, we determined, for each house-keeping node i, the number of its direct neighbors (i.e. D i, the degree centrality) and counted how many of these were house-keeping nodes (D HK, i ); we then defined P HK, i as the proportion of these direct neighbors that were house-keeping nodes: P HK, i = D HK, i /(D i ).
Given the residential history for case i, L i, further denote the space-time coordinate representing place of residence at time of diagnosis as, noting that ∈ L i We can then define that subset of the residential history L i over which the exposure window occurred as: Here t i, D is the time of diagnosis for individual i.
The reliability of bit c i is then defined as |L(c i )|.
A network is called sparse if the cardinality of N i is small for all i = 1, 2, …, I. Consider any block diagonal positive definite matrix (4) Π = diag { Π 1, …, Π I } with Π i ∈ C d i × d i, i = 1, …, I. Then define (5) Υ = Π 1 / 2 Ψ Π 1 / 2 and choose (6) 0 < γ < 2 ‖ Υ ‖ − 1.
The likelihood function for a single gene (in sample j of group i) is then defined as: Because P(R n = r n, I n = k| θ) = P(R n = r n, I n = k, L n = l k n | θ), we can write Expressing the joint probability as a product of conditional probabilities, we have where k = 1, ⋯, K.
The waiting times W Λ,x), W and W i are then defined as above, and often referred to as sooner waiting times.
