Sentence examples for optimal posterior probability from inspiring English sources

We used the posterior probability given by the ASR system as a confidence measure to prune the erroneous data, where all phones below an optimal posterior probability threshold were pruned.

Table 4 also shows the percentage of true acceptances (TA), false rejections (FR), true rejections (TR), and false acceptances (FA) obtained at the maximum and optimal posterior probability threshold value equal to 1.0 for all the four cases in Table 3.

It was shown how the MLSE algorithm can be used to determine the most likely sequence of estimates, while the MAP algorithm can be used to determine optimal posterior probabilities regarding the transmitted symbols or codewords.

If the priors on nuisance parameters match the true values of those parameters, Bayesian choice of topology will be unbiased and optimal, and the posterior probability of a topology will correspond to the probability that the tree is true given the data [32].

It is known that the Bayesian classifier is the optimal classifier when the posterior probability can be calculated.

The NDLM analysis will determine which of the doses studied is optimal (in terms of posterior probability of efficacy).

The optimal topology and Bayesian posterior probability values were obtained using Mr. Bayes version 3.1.2 [ 57] with two independent runs each of 1000000 generations.

Trees were then built using Mr. Bayes v. 3.1.2 [ 60] for Bayesian analysis to determine optimal tree topology and posterior probability values for the nodes, with 1 000 000 Markov Chain Monte Carlo generations and the burn-in value determined graphically by removing trees before the plateau.

By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully.

A Markov Chain Monte Carlo method is used to reach the optimal SNP partition with maximum posterior probability in BEAM.

It superimposes occupancy and binding affinity scores, Viterbi predictions for optimal nucleosome positioning and the posterior probability of a genomic position to be the start of a nucleosome.