In a similar manner, we define a location-scale regression model using the LPCNB regression model.
In the same manner, we define the mixture matrix in each frequency bin X f,:) = [X f,1),...,X f,N T )].
In the same manner, we define the generalized projection [17] for a nonempty closed convex subset of a strictly convex, smooth, and reflexive Banach space as follows.
In a similar manner, we define ( {V}_k^{boldsymbol{F}} ) as the absolute difference between ( {S}_{{boldsymbol{F}}_i} ) and ( {S}_{{boldsymbol{F}}_{d(i)}} ) as follows.
In a similar manner, we define D G 1, p for an open and G-symmetric subset of R N, that is, if x ∈ Ω, then g x ∈ Ω for all g ∈ G.
In a similar manner, we define the normalized instantaneous power gain G n (r n ) as follows: G n r n = 1 K d 0 d r e f γ H n r n (1) = G p l, n r n G f, n G s, n, (2) (3) (29).
In this manner, we define a fake separation factor (s_{H}^{R}), which indicates how well all available fake holograms are distinguishable from the genuine holograms of the hologram type intended to be faked, i.e., s_{H}^{R} := frac{max left{d_{cos}(g_{i},g_{j}) | g_{i},g_{j} in G_{H}^{R}, i ne j right}}{min left{ d_{cos}(f_{i},g_{j}) | f_{i} in F_{H}^{R},g_{j} in G_{H}^{R} right}}.
Since the genetic and epigenetic state of all the genes in a module change in the similar manner in reprogramming, we defined the genetic state and epigenetic state of a module by a single value respectively: with measures the epigenetic state and genetic state of module i at time k respectively.
Continuing this manner we can define a sequence as follows: (2.3).
In a similar manner, we can define the path integral for more general noise processes than the Wiener process.
