A semidefinite optimization problem is solved to estimate the number of independent disturbances entering the system and their covariances.
Equation 2 can be solved to obtain the estimate D, although this estimate differs little from that given by Equation 1.
Thus, in practice, the SVD reparameterized model can be solved to yield the estimate of the hyperparameters in a very efficient manner using the much smaller matrix form.
With iterative updates of the current estimate with the correction matrix, the final estimate can be solved to determine the mobile user position.
We show below how this partial knowledge of the phylogenetic relationship between samples affects the likelihood of the observed Y-STR lengths, and demonstrate how the resulting optimization problems can be solved to obtain maximum likelihood (ML) estimates of Y-STR mutation rates.
Cost equations are also solved, to estimate the cost of the infrastructure under analysis.
Then, another three MMV problems are solved to get the estimates in the β domain.
For each experimental result, x was solved to correct for PCR bias, and estimated data presented b values very close to 1, confirming the high level of correction of PCR bias that is obtained with the cubic equation based correction, compared with the raw pyrosequencing results (Table 1).
Finally, the remaining problem to be solved is to estimate the standard errors of the estimated measure as Kakwani, Wagstaff and van Doorslaer [6] proposed.
