نتایج جستجو برای: order latent variables insight
تعداد نتایج: 1331383 فیلتر نتایج به سال:
For many applications, the observed data may be incomplete and there often exist variables that are unobserved but play an important role in capturing the underlying relationships. In this work, we propose a method to identify local latent variables and to determine their structural relations with the observed variables. We formulate the local latent variable discovery as discovering the Markov...
This is a follow-up on two previous articles on WarpPLS and e-collaboration. The first discussed the five main steps through which a variance-based nonlinear structural equation modeling analysis could be conducted with the software WarpPLS (Kock, 2010b). The second covered specific features related to grouped descriptive statistics, viewing and changing analysis algorithm and resampling settin...
This paper focuses on service clustering and uses service descriptions to construct probabilistic models for service clustering. We discuss how service descriptions can be enriched with machine-interpretable semantics and then we investigate how these service descriptions can be grouped in clusters in order to make discovery, ranking, and recommendation faster and more effective. We propose usi...
How accurate are insights compared to analytical solutions? In four experiments, we investigated how participants' solving strategies influenced their solution accuracies across different types of problems, including one that was linguistic, one that was visual and two that were mixed visual-linguistic. In each experiment, participants' self-judged insight solutions were, on average, more accur...
This paper focuses on service clustering and uses service descriptions to construct probabilistic models for service clustering. We discuss how service descriptions can be enriched with machine-interpretable semantics and then we investigate how these service descriptions can be grouped in clusters in order to make discovery, ranking, and recommendation faster and more effective. We propose usi...
In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two n...
This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. To focus the discussion and make it manageable in scope, analysis of longitudinal data using growth models will be considered. Continuous latent variables are common in growth modeli...
Hierarchical latent class(HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are hidden. We explore the following two-stage approach for learning HLC models: One first identifies the shallow latent variables – latent variables adjacent to observed variables – and then determines the structure among the shallow and possibly some other “deep” late...
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