Sentence examples for expected likelihoods from inspiring English sources

Outcomes with expected likelihoods of less than 5% occur frequently in medical decision making situations and in many cases have an important impact on medical decisions.

initialisation: using pictorial structure type models as in [7], we localise all the targets along with the configuration of their parts, for iterations, (a) evolve particles using constrained genetic operators as in EPMCMC filter,   (b) the expected likelihood data association provides an efficient solution for data association of each object into the particle weights of the EPMCMC filter.  .

Two different tests, Shimodaira–Hasegawa (SH) and Expected Likelihood Weight (ELW), were applied to the two or three resulting topologies for each single or double event, respectively, to further verify the results obtained with RDP3.

c: confidence value (expected likelihood weigth).

cELW = Expected Likelihood Weight, SH = Shimodaira and Hasegawa, n.a = not applicable.

TREE-PUZZLE was used to perform the Expected Likelihood Weight (ELW) test and Shimodaira and Hasegawa (SH) tests.

Results from hypotheses testing based on the three alternative Acanthocephala-sistergroup relationships covered by the present approach using the Expected Likelihood Weight (ELW) test.

For example, Blangero et al. (2013) assume that σ is known in their derivations of the expected likelihood-ratio-test statistic (ELRT).

We used expected likelihood weighting [ 23], which provides a simple and intuitive method for making multiple comparisons of models and constructing corresponding confidence sets.

We used expected likelihood weighting [ 13], which provides a simple and intuitive method for making multiple comparisons of models and constructing the corresponding confidence sets.

The EM method involves cycling between two steps: (i) the 'E-step' in which the expected likelihood function is calculated, conditional on the observed data and the current estimates of the model parameters; and (ii) the 'M-step' in which the expected likelihood function is maximized over an unknown parameter.