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

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### 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

... 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

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### 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

... 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

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### 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

... 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

... 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

... 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

... 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

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### 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

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### 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

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