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In traditional finite mixture models, separate regression equations are specified for the mean of each component of the mixture.
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A recent work of [10] exploits the Gaussian mixture models to separate the rain streaks, achieving the state-of-the-art performance, however, still with slightly smooth background.
Among others, Bolt et al. (2002), Yamamoto (1995), and Jin and Wang (2014) introduced mixture models to separate test takers who show aberrant response behavior that corresponds to PD from test takers who respond to all items with full effort.
Buysse and colleagues (2004) tested linear and quadratic mixture models using separate models for the eszopiclone and placebo groups.
Other extensions include independent vector analysis, where the mixture model is separated into multiple layers, with dependent source components across the layers being grouped into a multidimensional source vector.
Since the time and rate are mathematically confounded in Markov models [they are simultaneously calculated as a product (Yang, 2006)], we use a mixture model approach to separate the weight parameters from the basic rate parameters and branch lengths (Pagel and Meade, 2004).
We use the method presented in [26] which employs two separate mixture models for the skin and non-skin classes.
In this setting, a "mixture of models" or an "indescribable" mixture model, which applies separate submodels to each subpopulation can be used to separate nonresponders from responders.
Estimating mixture models via mixture of polynomials.
Using Mixture Models for Collaborative Filtering.
Finite mixture models, Dirichlet process mixture models, and Pitman--Yor process mixture models make this assumption, as do all other infinitely exchangeable clustering models.
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