introduction: limited data are available on the relationship between nutritional status and tuberculosis. the aim of this study was to evaluate and compare the body mass index (bmi) and serum albumin level in patients with active tuberculosis (atb) and latent tuberculosis (ltb). materials and methods: a cross-sectional study was conducted on 17 patients newly diagnosed with pulmonary tb  who we...

Generative latent-variable models are important for natural language processing due to their capability of providing compact representations of data. As conventional maximum likelihood estimation (MLE) is prone to focus on explaining irrelevant but common correlations in data, we apply maximum reconstruction estimation (MRE) to learning generative latent-variable models alternatively, which aim...

Latent variable models are important tools to infer the underlying structure of a set of data. When we condition on observed data in Bayesian inference, we implicitly assume that the modeling assumptions are true and that the data can be considered a representative draw from the model. However, realistic data rarely agrees with these modeling assumptions. Especially when the observed data is hi...

Probabilistic latent topic models have recently enjoyed much success in extracting and analyzing latent topics in text in an unsupervised way. One common deficiency of existing topic models, though, is that they would not work well for extracting cross-lingual latent topics simply because words in different languages generally do not co-occur with each other. In this paper, we propose a way to ...

The Gaussian Process Latent Variable Model (GPLVM) is a non-linear variant of probabilistic Principal Components Analysis (PCA). The main advantage of the GPLVM over probabilistic PCA is that it can model non-linear transformations from the latent space to the data space. An important disadvantage of the GPLVM is its focus on preserving global data structure in the latent space, whereas preserv...

In this paper we present a fully Bayesian latent variable model which exploits conditional nonlinear (in)-dependence structures to learn an efficient latent representation. The latent space is factorized to represent shared and private information from multiple views of the data. In contrast to previous approaches, we introduce a relaxation to the discrete segmentation and allow for a “softly” ...

Latent variable modeling is a commonly used data analysis tool in social sciences and other applied fields. The most popular latent variable models are factor analysis (FA) and latent class analysis (LCA). FA assumes that there is one or more continuous latent variables – called factors – determining the responses on a set of observed variables, while LCA assumes that there is an underlying cat...

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...

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...