Multiple Imputation as usually implemented assumes that data are Missing At Random (MAR), meaning that the underlying missing data mechanism, given the observed data, is independent of the unobserved data.
Furthermore, the current implemented covarion models assume that rate switching between 'on' and 'off' states and among different 'on' rates are homogenous across sites and the tree, which may not be realistic.
In this context, stochastic optimization has been implemented by assuming that the statistics of the energy processes are known and that past observations can correct the energy forecasts [13 15].
The MAP estimate could be implemented by assuming that a higher-level neuronal population calculates the a posteriori distribution based on knowledge of both stimulus distribution across the experiment and firing rate distribution of the neuronal population (e.g. Zemel et al. 1998).
This is implemented by assuming that two residues i,j are in contact if and only if they share a high number of neighbors, i.e. there is a high number of residues which are in contact with both i and j.
Motivated by Song et al. [32], COPMA can be implemented distributively as follows: Assume that each node pair m in the network has an unique ID m and maintains two variables P m c u r r e n t and P m u p d a t e d which are the transmit power of the m th node pair prior to and after the change of transmit beamformer, respectively.
In the simplest case, as implemented in RNAwolf, we may assume that ϵb ij only depends on the pair type θ for permitted combinations of pair types and is +∞ otherwise.
Until regulations or other mandatory measures are implemented, there's little reason to assume that would change.
When spatial multiplexing is implemented, we assume that the cooperative link has sufficient bandwidth for information transmission, thus cooperation is supported without any major interference issue.
The LD method as implemented here assumes that loci are independent (probability of recombination = 0.5).
