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[PDF] Top 20 Latent-Variable Modeling: Algorithms, Inference, and Applications

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Latent-Variable Modeling: Algorithms, Inference, and Applications

Latent-Variable Modeling: Algorithms, Inference, and Applications

... These latent (hidden) variables complicate finding a concise representation, as they introduce confounding depen- dencies among the variables of ...of latent phenomena in statistical modeling via ... See full document

198

Imprecise probabilistic graphical models: Equivalent representations, inference algorithms and applications

Imprecise probabilistic graphical models: Equivalent representations, inference algorithms and applications

... efficient algorithms to compute with ...efficient algorithms for such a purpose (unless P=NP): in fact, using the con- servative updating rule to do efficient classification with Bayesian networks is shown ... See full document

159

Inference and learning in state-space point process models:
algorithms and applications

Inference and learning in state-space point process models: algorithms and applications

... VB provides a neat, deterministic way for approximating the joint posterior distribution online. We have compared the performance of the VB filter to a stochastic approxima- tion method through a standard PF and seen ... See full document

203

Three Contributions to Latent Variable Modeling

Three Contributions to Latent Variable Modeling

... some applications, K is specified a priori based on some prior ...Moreover, inference conditioning on a specific K from the 2-stage approach clearly ignores the uncertainty in the selection process (Yang et ... See full document

116

A deterministic inference framework for discrete nonparametric latent variable models:learning complex probabilistic models with simple algorithms

A deterministic inference framework for discrete nonparametric latent variable models:learning complex probabilistic models with simple algorithms

... clustering algorithms nearly as simple as K-means which over- come most of its challenges and can infer the number of clusters from the ...and applications such as phenotyping Parkinson and Parkisonism ... See full document

167

Variational algorithms for Bayesian inference in latent Gaussian models

Variational algorithms for Bayesian inference in latent Gaussian models

... cal applications the model parameters (the regression coefficients) have an a priori (spa­ tial) pattern, because they express effects that are (spatially) correlated ... See full document

119

Geometric Inference in Bayesian Hierarchical Models with Applications to Topic Modeling

Geometric Inference in Bayesian Hierarchical Models with Applications to Topic Modeling

... variational inference in their respective original formulations scale well to large corpora of millions of ...2013) algorithms have been developed. Online algorithms for Hierarchical Dirichlet ... See full document

134

Inference and Interpretability in Latent Variable Modeling

Inference and Interpretability in Latent Variable Modeling

... CHAPTER I Introduction With the advent of technology, a large amount of today’s data is generated by means of complex mechanisms. Data may be available in various forms - for example, unlabelled data as in images, ... See full document

303

Learning latent variable models : efficient algorithms and applications

Learning latent variable models : efficient algorithms and applications

... a latent variable model from data, but the number of latent states required by a user is too small to accurately represent the training ...of latent states to compre- hensively describe the ... See full document

185

A gaussian process latent variable model for BRDF inference

A gaussian process latent variable model for BRDF inference

... Abstract The problem of estimating a full BRDF from partial ob- servations has already been studied using either paramet- ric or non-parametric approaches. The goal in each case is to best match this sparse set of input ... See full document

9

Modeling Latent Dynamic in Shallow Parsing: A Latent Conditional Model with Improved Inference

Modeling Latent Dynamic in Shallow Parsing: A Latent Conditional Model with Improved Inference

... the latent-dynamics ...that modeling this intermediate structure is ...the latent conditional model explicitly learn ...(BLP) inference algo- rithm, which is able to produce the most probable ... See full document

8

Latent Variable Modeling of Differences and Changes with Longitudinal Data

Latent Variable Modeling of Differences and Changes with Longitudinal Data

... USING LATENT-CHANGE CONCEPTS In any data analysis problem where multiple constructs have been measured at multiple oc- casions, we need to consider the importance of causal sequences and determinants of changes ... See full document

29

Latent Variable Models with Applications to Spectral Data Analysis

Latent Variable Models with Applications to Spectral Data Analysis

... Table 1 contains all the experimental results when K ranges from 1 to 10. These experimental results validate that the optimal hybrid models achieve the best prediction results when K is 9. Table 1 also shows that the ... See full document

64

Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs

Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs

... (and latent variables as well if present) include the “learning from measurements” (Liang et ...discrimination latent Dirichlet allocation (MedLDA) (Zhu et ...Bayesian latent variable ... See full document

49

Latent Variable Modeling of String Transductions with Finite State Methods

Latent Variable Modeling of String Transductions with Finite State Methods

... additional algorithms for compiling U θ from a set of arbitrary feature templates, 25 in- cluding templates whose features consider windows of variable or even unbounded ... See full document

10

Bayesian modeling and inference for asymmetric responses with applications

Bayesian modeling and inference for asymmetric responses with applications

... a latent variable formulation, we use a generalized extreme value (GEV) link to model multivariate asymmetric spatially- correlated binary responses that also exhibit non-random missingness, and show how ... See full document

142

Bayesian latent variable methods for longitudinal processes with applications to fetal growth

Bayesian latent variable methods for longitudinal processes with applications to fetal growth

... Directions Latent variable methods provide a flexible approach for complex modeling of correlation in longitudinal ...using latent variables to aggre- gate multiple ultrasound measurements and ... See full document

150

Robustness in Latent Variable Models

Robustness in Latent Variable Models

... in Latent Variable ...involving latent variables are widely used in many areas of applications, such as biomedical science and social ...statistical inference, certain distri- butional ... See full document

90

Latent Variable PCFGs: Background and Applications

Latent Variable PCFGs: Background and Applications

... rely heavily on linguistic knowledge of English, and as such they do not generalize to treebanks in other languages. With all of this previous work, nonterminal re- finement is central to the underlying parsing for- ... See full document

12

Latent-Variable PCFGs: Background and Applications

Latent-Variable PCFGs: Background and Applications

... rely heavily on linguistic knowledge of English, and as such they do not generalize to treebanks in other languages. With all of this previous work, nonterminal re- finement is central to the underlying parsing for- ... See full document

12

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