Mixtures of Linear Latent Variable ModelsKlaus K. Holst
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 ...
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 t...
Mixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, allowing not only the mean function but the
Process systems engineering (PSE) in (bio-)chemical engineering is the development of systematic techniques for process modeling, design, and control (Sargent, 1983). Current advances in machine learning (ML) and artificial intelligence (AI) have a significant impact on PSE (Venkatasubramanian, 2018...
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 ...
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 ...
The mixing of the experts is determined by a latent variable, the distribution of which depends on the same covariates used in the regressions. The expert error terms are assumed to follow the generalized t distribution, a rather flexible parametric form encompassing the standard t and normal ...
polybrominated diphenyl ethers and 1 polybrominated biphenyls) and 11 PFAS and the risk of breast cancer in a case–control study nested in the E3N French prospective cohort by performing two methods: Principal Component Regression (PCR) models, and Bayesian Kernel Machine Regression (BKMR) models....
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 ...