Sentence examples for mixture modelling are from inspiring English sources

Statistical speech models and a probabilistic technique called Gaussian mixture modelling are then used to identify each phoneme, before reconstructing the original word.

Latent growth mixture modelling was used to examine the course of fatigue over time.

Mixture models are important modeling approaches that account for data heterogeneity.

Behaviours identified by our mixture model are commuting (blue), foraging (black) and resting (red).

So, mixture models are great, but they have a number of limitations.

Moreover, mixture models are an interesting and flexible model family.

The Gaussian distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process.

However, existing mixture models are constrained by assuming an known number of sub-populations.

Different issues related to the numerical estimation of mixture models are also discussed.

The parameters of finite mixture models are estimated by the GA-based parameter estimation method.

Most of the existing mixture models are unable to accommodate these two aspects of the data.