Our method aims to infer the pathways from the bio-molecular interaction network and gene expression data by maximizing the information flow the target receives with the constraints of dissipation, saturation and direction.
In this paper, linear data reconciliation is reformulated by maximizing the information entropy to obtain probability distributions of the data with the minimum incorporation of prior knowledge.
Also, by maximizing the information content, models that use all the available data are intrinsically more powerful than models that use only one data type.
The M-Step obtained by maximizing the expected data log-likelihood is given by following expressions P ( w j | z k ) = ∑ d i ∈ D n ( d i, w j ) P ( z k | d i, w j ) ∑ w m ∈ W ∑ d i ∈ D n ( d i, w m ) P ( z k | d i, w m ) P ( z k | d i ) = ∑ w j ∈ W n ( d i, w j ) P ( z k | d i, w j ) n ( d i ).
With the state sequence, the static spectral and excitation features are determined by maximizing the joint data-likelihood of the combined static and dynamic feature streams.
Find new values for the parameters (boldsymbol {theta }^{1}=left (pi ^{1},boldsymbol {beta }_{1}^{1},sigma _{1}^{1},boldsymbol {beta }_{2}^{1},sigma _{2}^{1}right)) by maximizing the complete-data likelihood 5.
Usually, in direct optimization analyses, the choice of weighting values for gaps (opening and extension) and substitutions is driven by maximizing the among-data congruence [ 100].
SM is a MIMO technique aimed at maximizing the data throughput by exploiting the degrees of freedom in MIMO channels.
Spatial multiplexing is a MIMO technique aimed at maximizing the data throughput by exploiting the degrees of freedom in MIMO channels.
