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To understand the effect of grain velocities, we examine Model H, in which we use Eq. (10) and vary ac.
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It shows that forecast model (H) is a weighted vote of T weak models (h) (in) h where (alpha) is the weight assigned to each.
On the contrary, the model H is in better agreement with the mean model at larger SH orders but agrees less well at low SH orders.
The model value D model(h) is defined in equation (3) and written in terms of the refinement parameters.
Note that (hat {underline {mathbf {h}}}^{(p_{mathrm {d}})}{ kappa)}) models h d(k) in Fig. 5 and (hat {underline {mathbf {h}}}^{(p)}{ kappa)}forall p neq {p}_{mathrm {d}}) jointly model h c(k) in Fig. 5.
In this model, H(a) measures the total amount of control information required for selecting action "a" and is processed in the premotor cortex.
In this model, h TERT-immortalized human mammary epithelial cells are transduced with SV40 Large T antigen (Zhao et al., 2003).
Assign channel gain of (n in mathbb {N}) (in linear deterministic model) and (h in mathbb {C}) (in Gaussian model) to the links of the subgraph and channel gain of 0 to the links that are not in the graph of Fig. 11b.
Assign channel gain of (n in mathbb {N}) (for the linear deterministic model) and (h in mathbb {C}) (for the Gaussian model) to all edges in this path.
For all other links in the network, assign a channel gain of (n in mathbb {N}) (in the linear deterministic model) and (h in mathbb {C}) (in the Gaussian model).
Consider a single-layer (K,m) folded-chain network in which the channel gain of a link from S i to destinations i,i+1,…,m is equal to (n in mathbb {N}) (for the linear deterministic model) and (h in mathbb {C}) (for the Gaussian model), i=1,2,…,m, and all other channel gains are equal to zero.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com