Sentence examples for variable model for from inspiring English sources

To capture the non-stationary distribution of DT, we utilize the Gaussian process latent variable model for dimensional reduction.

It is shown that the data are reasonably consistent with a physically based internal state variable model for recrystallization.

The aspect model is a latent variable model for co-occurrence data that associates an unobserved class variable to each observation (i.e., words and documents).

We adopt geoadditive latent variable model for binary/ordinal indicators to analyze the influence of variables of different types on the morbidity among young children in Nigeria.

A manganese dependent, internal state variable model for athermal martensite transformation was used to estimate the strains that develop in two layers of differing manganese content which maintain a coherent interface.

Previously conducted studies of the flamelet/progress variable model for the prediction of nonpremixed turbulent combustion processes identified two areas for model improvements: the modeling of the presumed probability density function (PDF) for the reaction progress parameter and the consideration of unsteady effects [Ihme et al., Proc. Combust. Inst. 30 (2005) 793].

To develop biomechanical variable models for driver skull base injury mechanisms in motor vehicle collisions.

Questions related to the accuracy of the estimates with random process models vis-a-vis random variable models for the temperature fluctuations are examined.

In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics.

Among these methods, topic models, such as latent Dirichlet allocation (LDA) [12] and probabilistic latent semantic analysis (pLSA) [13], that consider probabilistic latent variable models for hierarchical learning have caused extensive interest.

In addition, the CPM is integrated with the dynamic principal component analysis and the linear Gaussian state space model to build improved latent variable models for nonlinear and dynamic process monitoring.