Sentence examples for mixture model using from inspiring English sources

In this paper, we attempt to find the optimum designs for testing the presence of synergistic effects in a mixture model using the maximin criterion.

However, the algorithms of mixture model using expectation maximization (EM) technique are still too slow to deal with real million-node networks, since they compute hidden variables on the entire network in each iteration.

This article deals with the modeling of the heterogeneity existing in the lifetime processes using the mixture of the inverse Rayleigh distribution, and the spotlight is the Bayesian inference of the mixture model using non-informative (the Jeffreys and the uniform) and informative (gamma) priors.

Class membership was obtained from the mixture model using an empirical Bayes procedure, and subsequent associations with invasive stage were tested via permutation test with 10,000 permutations each using the standard chi-square goodness-of-fit test.

By fitting the parameters of a mixture model (using an expectation-maximization algorithm), each node is assigned a probability of belonging to any one group that has been identified.

Subsequently, we train the mixture model using the EM algorithm.

We believe that the local mixture model used in LIDE may provide an interesting method for fusioning multi-exposure images.

We believe that the mixture model used in LIDE can be extended to handle this multi-scale enhancement approach nicely.

The mixture model uses prior knowledge to specify meaningful priors.

Further details of the Bayesian hierarchical mixture model used here can be found in Parmigiani et al. [ 8].

In Section 'Methods', the nonparametric mixture model used for the cluster analysis is presented in parallel with the associated likelihoods.