Sentence examples for maximum likelihood solutions from inspiring English sources

This method develops a dynamic programming algorithm, which is theoretically guaranteed to find exact maximum likelihood solutions of the variable order Markov chain model for haplotype inference problem within linear running time.

Then, the maximum likelihood solutions of the external parameters can be obtained by calculating the minimum value of the formula {displaystyle sum_{i=1}^m{leftVert Gleft({V}_1^{prime },{V}_2,{X}_i^{prime}right -{mme}right)-{m}_i^{prime}rightVert}^2} (6).

Assuming that there are n images in the calibration board, each image has m corner points, and all the unknown parameters' maximum likelihood solutions can be obtained by calculating the minimum value of the formula {displaystyle sum_{i=1}^n{{displaystyle sum_{j=1}^mleftVert Gleft({V}_{1 i},{V}_2,{X}_{i{V}_4,{X}_{i j}right -{m}_{i j}right -{m}}^2} (5).

The limdil function of the statmod package for R was used to find the maximum likelihood solutions of the frequencies of the CAFCs.

Any extensions of this approach so far (i.e. two-part or mixtures approaches) have been implemented using Markov Chain Monte Carlo (MCMC) methods, since maximum likelihood solutions may be difficult to obtain (Lambert et al., 2008).

With regard to the CAFC frequencies, data of day-14 cultures for 150 days post irradiation varied so much that the limdil function failed to find maximum likelihood solutions.

Therefore, the FEMU approaches do not provide the "maximum likelihood solution".

Optimal virtual fields exist, thus minimizing the uncertainty and providing the "maximum likelihood solution".

The maximum likelihood solution finds the values (X_{q}) that maximize the probability in Eq. (7).

In particular, it provides an analytical maximum likelihood solution that is found iteratively.

Two algorithms stand out in this study: first, Zhang's method [19] that presents a maximum likelihood solution.