Mixtures of Linear Latent Variable ModelsKlaus K. Holst
Mixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, allowing not only the mean function but the
Mixtures of regression models (MRMs) are widely used to investigate the relationship between variables coming from several unknown latent homogeneous groups. Usually, the conditional distribution of the response in each mixture component is assumed to be (multivariate) normal (MN-MRM). To robustify ...
For example, mixed mem- bership stochastic blockmodels (MMSB)6 were proposed to discover complex network structure in a variety of applications, e.g., large-scale protein interaction network and social network. The MMSB develops a novel class of latent variable models for relational data, and ...
Similarly, we did not evaluate consumer product use because of the limited information available for this variable. In constructing our DAG, we therefore considered both diet and consumer product use to be latent. Among variables ascertained through household interviews, we reasoned that age (Kato ...
latentAntibodyoutcome (β = − 0.16, 95% CI: − 0.26, − 0.05), but theMetalsvariable was characterized by positive and negative loadings of W-As and B-Pb, respectively. Sex-stratified MLR and SEM analyses showed W-As and B-Pb associations were exclusive to females. ...
EM is commonly employed for MLE optimiza- tion in the case where directly maximizing the data likelihood for the sought after variable is intractable, but maximizing the expected joint data likelihood conditioned on a set of latent variables is tractable. For the registration case, the sought ...
Structural Equation ModelsComparative AnalysisSample SizeThe accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) 脳 3 (exogenous latent mean difference) 脳 3 (endogenous latent mean difference) 脳 3 (correlation between factors) 脳 ...
In particular, latent variable models can quantify an individual鈥檚 cumulative exposure burden to mixtures and identify hidden subpopulations with distinct exposure patterns. Here, we first provide a review of measurement approaches from the psychometrics field, including structural equation modeling and ...
Then, we learned clustered latent space representations of surface descriptors by using mixtures distributions for Gaussian process latent variable models to avoid computing similarity measures, which classify the resulting latent vectors to establish group-wise correspondences. The experimental results show ...