We are interested in the inference of network topology from temporal expression profiles by minimizing the conditional entropy between the genes, i.e., the gene entropy conditioned to the state of others genes.
We design an importance sampling scheme for backward stochastic differential equations (BSDEs) that minimizes the conditional variance occurring in least-squares Monte-Carlo (LSMC) algorithms.
We are interested in estimating α by trying to minimize the conditional sum of squares Q = ∑ t = 1 T d H 2 ( X t, α X t − 1 ).
The maximum mean discrepancy distance measure is modified to measure the distance between the conditional distributions and is integrated into the PCA optimization algorithm to minimize the conditional distributions.
By formulating a constrained optimization model, we address the problem of optimal reinsurance design using the criterion of minimizing the conditional tail expectation (CTE) risk measure of the insurer's total risk.
Taking CSI mismatch of H 1 and H 2 into account, the proposed objective function is to minimize the conditional MSE between the transmitted and received signals subject to relay power constraint, which is given as follows: begin{array}{*{20}l} &min_{mathbf{Q},mathbf{W}} Eleft[parallelmathbf{widehat{s}}-mathbf{s}{parallel_{2}^{2}}|widehat{mathbf{H}_{1}},widehat{mathbf{H}_{2}}right], end{array} (9a).
Similar to MCG, the H matrix could be used to minimize the conditional variances i.e. the MCH method.
MCA and MCG minimize the conditional genetic variance in the target population based on, respectively, the numerator relationship matrix A and the genomic G matrix calculated from the sparse genotypes.
The model is trained discriminatively, minimizing the conditional negative log-likelihood of labels over the empirical distribution of the training data: (3) subject to the regularization constraint ‖ λ‖1≤β.
If we are interested in classification of cancer based on a gene pair, then we wish to select the two genes that minimize the conditional entropy H(C| G1, G2), or, equivalently, maximize the mutual information I(G1, G2; C).
