Topic
Latent variable model
About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.
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TL;DR: In this article, the authors provide estimates of reputation as a dynamic latent variable that is determined by price premiums and market data, and analyze the effect of extrinsic factors on reputation.
Abstract: The present study provides estimates of reputation as a dynamic latent variable that is determined by price premiums and market data. Further, it analyzes the effect of extrinsic factors on reputation. Specifically, this study seeks to (i) quantify the reputation of Washington apples over time, (ii) study the dynamic nature of reputation, and (iii) analyze the effect of the label "Washington Apple." The model adopted in this study is the dynamic multiple-indicator multiple-cause (DYMIMIC) modeling approach, which is a special case of the general latent variable modeling scheme called "state-space" models. Both DYMIMIC and hedonic approaches are applied to the data on Washington apples, and results from the two approaches are compared.
58 citations
TL;DR: An orthogonal set of latent variables representing commonalities in the brain-behavior system are obtained, which emphasize specific neuronal networks involved in cognitive ability differences and the early lifespan maturation of the covariance between cognitive abilities and local gray matter volume.
58 citations
TL;DR: The experimental results on the Carnegie Mellon University motion capture data demonstrate the advantages of the proposed multilayer models over several existing GP-based motion models in terms of the overall performance of human gait motion modeling.
Abstract: We present new multilayer joint gait-pose manifolds (multilayer JGPMs) for complex human gait motion modeling, where three latent variables are defined jointly in a low-dimensional manifold to represent a variety of body configurations. Specifically, the pose variable (along the pose manifold) denotes a specific stage in a walking cycle; the gait variable (along the gait manifold) represents different walking styles; and the linear scale variable characterizes the maximum stride in a walking cycle. We discuss two kinds of topological priors for coupling the pose and gait manifolds, i.e., cylindrical and toroidal, to examine their effectiveness and suitability for motion modeling. We resort to a topologically-constrained Gaussian process (GP) latent variable model to learn the multilayer JGPMs where two new techniques are introduced to facilitate model learning under limited training data. First is training data diversification that creates a set of simulated motion data with different strides. Second is the topology-aware local learning to speed up model learning by taking advantage of the local topological structure. The experimental results on the Carnegie Mellon University motion capture data demonstrate the advantages of our proposed multilayer models over several existing GP-based motion models in terms of the overall performance of human gait motion modeling.
58 citations
TL;DR: The latent class model for mixed binary and metric variables is extended to accommodate any type of data (including ordinal and nominal) and its use in Archaeometry for classifying archaeological findings/ objects into groups is discussed.
58 citations
TL;DR: A Bayesian Markov chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model, with Bayes factors used to discriminate between different distributional assumptions for durations.
58 citations