Latent variable
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Mean-field variational approximate Bayesian inference for latent variable models
... sampling; Latent variable mod- els; Marginal distribution; Mean-field variational methods 1 Introduction Bayesian inference typically requires the computation of the posterior distribution for a collection ...
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Monitoring of a carbon anode paste manufacturing process using machine vision and latent variable methods
... Multivariate latent variable statistical methods such as principal component analysis (PCA) and projection to latent structures (PLS) were use to analyse the image ...
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Missing rating imputation based on product reviews via deep latent variable models
... on latent Dirichlet allocation (LDA, Blei et ...deep latent variable model for both the ordinal matrix encoding the user/product scores, and the document-term matrix en- coding the ...
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Latent Variable Models for Stochastic Discount Factors
... ABSTRACT Latent variable models in finance originate both from asset pricing theory and time series ...of latent structures, which are both useful to reduce the dimension of a statistical model ...
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Latent Variable Models for Stochastic Discount Factors
... Latent variable models in finance originate both from asset pricing theory and time series ...of latent structures, which are both useful to reduce the dimension of a statistical model specified for ...
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Ising distribution as a latent variable model
... VI. DISCUSSION I have proposed a reformulation of the Ising distribu- tion as a latent variable model, and used it to derive prin- cipled approximations by the simpler Cox distribution. In practical ...
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Latent variable models for stochastic discount factors
... ABSTRACT Latent variable models in finance originate both from asset pricing theory and time series ...of latent structures, which are both useful to reduce the dimension of a statistical model ...
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Latent variable language models
... the latent variable. However, the latent variable has limited capacity, and may model only the aspects that contribute to most of the reconstruction loss (abstract concepts) and there may be ...
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Bayesian nonparametric latent variable models
... Learning latent variable models, such as clustering terrains in a feature space, is a difficult ...of latent surfaces representing the data and this translated into accurate ...
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Blind regression : nonparametric regression for latent variable models via collaborative filtering
... their corresponding rating 𝑦(𝑢, 𝑖) is a noisy measurement of 𝑓 (𝑥 1 (𝑢), 𝑥 2 (𝑖)) for some unknown function 𝑓 . In contrast to classical regression, the features 𝑥 = (𝑥 1 (𝑢), 𝑥 2 (𝑖)) are not observed (latent), ...
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Non-destructive quality control of carbon anodes using modal analysis, acousto-ultrasonic and latent variable methods
... to Latent Structure (PLS) ...to Latent Structures (PLS) regression was also used to correlate acousto-ultrasonic features and textural descriptors collected from corresponding X-ray CT-Scan ...
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General and Specific Contributions of RAN to Reading and Arithmetic Fluency in First Graders: A Longitudinal Latent Variable Approach
... The Present Study To date, no studies have explored the latent structure of RAN and whether one or several RAN factors are needed to predict academic outcomes, such as reading and arithmetic. Investigating this ...
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Temporal Analysis of Text Data Using Latent Variable Models
... The latent factors z of the model can be seen as topics that are present in the data. The parameters of each topic can be used as descriptions of the topic. P (t|z) represents the probabilities of the terms for ...
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Forests of hierarchical latent models for association genetics
... observed variable and a target variable such as the phenotype, conditional on the latent variable, parent of the observed ...phenotype variable is not included in the ...
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Latent variables and the network perspective
... a latent variable is mainly therapeutic rather than ...hidden variable is often seen as a therapeutic target rather than an etiological node; that is, not something to find that would explain ...
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Mixture models, latent variables and partitioned importance sampling
... Latent variable Gibbs sampler 1. Generate Z i ( i = 1 ;:::;n ) from (4), 2. Generate j ( j = 1 ;:::;k ) from (3). This sampler is quite easy to implement, retaining an iid structure in each iteration, and ...
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Scale-invariant probabilistic latent component analysis
... plete latent variable decomposition of counts data. In Neural Information Processing Systems, Vancouver, BC, Canada, December 2007. [10] Paris Smaragdis and Judith C. Brown. Non-negative matrix ...
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Decentralized Topic Modelling with Latent Dirichlet Allocation
... particular Latent Dirichlet Allocation (LDA) [5] model to decentralized ...for latent variable models, which adapts online EM [7] with local Gibbs sampling in the case of intractable latent ...
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GAZE LATENT SUPPORT VECTOR MACHINE FOR IMAGE CLASSIFICATION
... GAZE LATENT SUPPORT VECTOR MACHINE FOR IMAGE CLASSIFICATION Xin Wang Nicolas Thome Matthieu Cord Sorbonne Universit´es, UPMC Univ Paris 06, UMR 7606, LIP6, F-75005, Paris, France This paper deals with image ...
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Online EM Algorithm for Latent Data Models
... to latent variable models of independent ...Keywords: Latent data models, Expectation-Maximisation, adaptive algorithms, online estima- tion, stochastic approximation, Polyak-Ruppert averaging, ...
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