Sentence examples for sum of likelihood from inspiring English sources

Rather, this table focuses on those inclusion modes that are deemed the most important per constituency, where importance is defined as the sum of likelihood and impact.

Remark 3. Within our framework, a crude non-Bayesian single frame maximum likelihood target detector could be built by simply evaluating the likelihood map for each aspect state and finding the maximum over the image grid of the sum of likelihood maps weighted by the a priori probability for each state (usually assumed to be identical).

The test statistic proposed by these authors is calculated for each tree,, in a large collection of trees (including at least the ML trees for all markers), and is the sum of likelihood ratios for each marker between the likelihood calculated under the ML tree and the tree in question (eq. 3 ).

Recall that the likelihood of a read or a read pair is the sum of likelihoods of individual alignments.

Overall dataset The overall public domain dataset (7) thus contains between one and four records for each death, with the sum of likelihoods for each individual being unity.

Overall dataset The overall public-domain dataset (8) thus contains between one and four records for each death, with the sum of likelihoods for each individual being unity.

Overall dataset The overall public-domain dataset (10) thus contains between one and four records for each death, with the sum of likelihoods for each individual being unity.

Overall dataset The overall public-domain data set (30) thus contains between one and four records for each death, with the sum of likelihoods for each individual being unity.

We calculated the relative contribution of each predictor using the differences between the log likelihood of the full model and the log likelihood of a model without each of the predictors, and the relative contribution of each predictor was defined as the ratio of its log likelihood difference to the sum of the likelihood differences from all predictors × 100 [ 20].

The relative contribution of each predictor was calculated using the differences between the log likelihood of the full model and the log likelihood of a model without each of the predictors and was defined as the ratio of its log likelihood difference to the sum of the likelihood differences from all predictors × 100 [ 25].

Given a training set of enzymes and their catalytic residue annotations, we estimate the parameters (b, w 1, w 2) using a regularized maximum likelihood approach in which we maximize the sum of the likelihood and an L1 penalty term: where w =(w 1, w 2) and ‖ w ‖1=∑ k | w k | is the L1 norm.