Results 31 to 40 of about 6,110,460 (312)
Auxiliary Guided Autoregressive Variational Autoencoders [PDF]
, 2018 Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics
Lucas, Thomas, Verbeek, Jakob
core +4 more sources
Asymptotic Accuracy of Bayesian Estimation for a Single Latent Variable [PDF]
, 2015 In data science and machine learning, hierarchical parametric models, such as mixture models, are often used. They contain two kinds of variables: observable variables, which represent the parts of the data that can be directly measured, and latent ...
Yamazaki, Keisuke
core +1 more source
The Inflation Technique for Causal Inference with Latent Variables [PDF]
Journal of Causal Inference, 2016 The problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the inflation
Elie Wolfe, R. Spekkens, Tobias Fritz
semanticscholar +1 more source
Foundations of structural causal models with cycles and latent variables [PDF]
Annals of Statistics, 2016 Structural causal models (SCMs), also known as (non-parametric) structural equation models (SEMs), are widely used for causal modeling purposes. In particular, acyclic SCMs, also known as recursive SEMs, form a well-studied subclass of SCMs that ...
S. Bongers +3 more
semanticscholar +1 more source
Fitting Nonlinear Structural Equation Models in R with Package nlsem
Journal of Statistical Software, 2017 Structural equation mixture modeling (SEMM) has become a standard procedure in latent variable modeling over the last two decades (Jedidi, Jagpal, and DeSarbo 1997b; Muthén and Shedden 1999; Muthén 2001, 2004; Muthén and Asparouhov 2009).
Nora Umbach +3 more
doaj +1 more source
Pyramid-VAE-GAN: Transferring hierarchical latent variables for image inpainting
Computational Visual Media, 2023 Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with reasonable structures, rich detail, and sharpness at the same time. In this paper, we propose the Pyramid-VAE-
Huiyuan Tian +4 more
doaj +1 more source
, 2018 Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent
Yujiao Mai, Z. Zhang, Zhonglin Wen
semanticscholar +1 more source
Scientia Agricola, 2006 The study of soil property relationships is of great importance in agronomy aiming for a rational management of environmental resources and an improvement of agricultural productivity.
Luís Carlos Timm +5 more
doaj +1 more source
Proper elimination of latent variables [PDF]
, 1997 We consider behaviors in which we distinguish two types of variables, manifest variables, the variables that are of interest to the user and latent variables, the variables that are introduced to obtain a first representation.
Polderman, Jan Willem
core +3 more sources
Model-Based Manifest and Latent Composite Scores in Structural Equation Models
Collabra: Psychology, 2019 Composite scores are commonly used in the social sciences as dependent and independent variables in statistical models. Typically, composite scores are computed prior to statistical analyses.
Norman Rose +3 more
doaj +1 more source