A geometric rate of convergence in probability is proved for these iterative estimates to MLE.
For the binary input case, an iterative estimating method is used in [50].
The empirical initial variances and their iterative estimates for the measurements and the process noise factors are plotted in Figs 4, 5, 6, 7, 8, 9 and 10.
Our method is essentially based on establishing sharper estimates for increasing positive solutions of (1.1) than (1.6), using an iterative technique; and to obtain analogous iterative estimates for decreasing positive solutions of (1.1).
Now, the procedure may be repeated until convergence, where new posteriors are computed under the updated prior variance τ ^ 2. Figure 2(b) shows an example of central Gaussian priors that result from iterative estimates of τ 2. The rationale behind this iterative procedure is as follows.
Imputation is achieved using an iterative estimation process; estimates of later iterations may be based on the estimates calculated in previous iterations.
As for iterative estimation, one can estimate the energy ratios between multiple speakers instead of the SNR in the two-speaker case and adapt the speaker models accordingly.
By applying a new fixed point theorem, we obtain the existence, uniqueness, iterative approximation, and error estimate of solutions for these functional equations.
The main results are presented in Section 3. By applying the new fixed point theorem, we establish the existence, uniqueness, iterative approximation, and error estimate of solutions for the functional equation (1.3) and (1.4).
However a mean resistance value was estimated (by iterative approximation at two time points) in this study.
