Results 291 to 300 of about 6,143,323 (339)
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Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
European Conference on Computer Vision, 2020In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants.
S. Casas +5 more
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Bayesian estimation of single and multilevel models with latent variable interactions
Structural Equation Modeling: A Multidisciplinary Journal, 2020In this article, we discuss single and multilevel SEM models with latent variable interactions. We describe the Bayesian estimation for these models and show through simulation studies that the Bayesian method outperforms other methods such as the ...
T. Asparouhov, B. Muthén
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Worse than measurement error: Consequences of inappropriate latent variable measurement models.
Psychological methods, 2020Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model. We explore what happens when data
M. Rhemtulla, R. van Bork, D. Borsboom
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Statistical and Econometric Methods for Transportation Data Analysis, 2019
We make the following striking observation: fully convolutional VAE models trained on 32×32 ImageNet can generalize well, not just to 64×64 but also to far larger photographs, with no changes to the model.
Simon Washington +3 more
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We make the following striking observation: fully convolutional VAE models trained on 32×32 ImageNet can generalize well, not just to 64×64 but also to far larger photographs, with no changes to the model.
Simon Washington +3 more
semanticscholar +1 more source
Measurement: Interdisciplinary Research & Perspective, 2008
This paper formulates a metatheoretical framework for latent variable modeling. It does so by spelling out the difference between observed and latent variables. This difference is argued to be purely epistemic in nature: We treat a variable as observed when the inference from data structure to variable structure can be made with certainty and as latent
openaire +3 more sources
This paper formulates a metatheoretical framework for latent variable modeling. It does so by spelling out the difference between observed and latent variables. This difference is argued to be purely epistemic in nature: We treat a variable as observed when the inference from data structure to variable structure can be made with certainty and as latent
openaire +3 more sources
Dynamic Probabilistic Latent Variable Model for Process Data Modeling and Regression Application
IEEE Transactions on Control Systems Technology, 2019Dynamic and uncertainty are two main features of the industrial process data which should be paid attention when carrying out process data modeling and analytics.
Zhiqiang Ge, Xinru Chen
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Latent Variable Centering of Predictors and Mediators in Multilevel and Time-Series Models
Structural Equation Modeling: A Multidisciplinary Journal, 2018Latent Variable Centering of Predictors and Mediators in Multilevel and Time-Series Models Tihomir Asparouhov & Bengt Muthén To cite this article: Tihomir Asparouhov & Bengt Muthén (2018): Latent Variable Centering of Predictors and Mediators in ...
T. Asparouhov, B. Muthén
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Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review
Industrial & Engineering Chemistry Research, 2018Dimensionality reduction is important for the high-dimensional nature of data in the process industry, which has made latent variable modeling methods popular in recent years.
Zhiqiang Ge
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