We discuss how latent variable models are useful to deal with the complexities of big data from different perspectives: simplification of data structure; flexible representation of dependence between variables; reduction of selection bias. Problems involved in parameter estimation are also discussed
Many driving factors of physical systems are often latent or unobserved. Thus, understanding such sy...
Although SD modeling is sometimes called theory-rich data-poor modeling, it does not mean SD modelin...
The pursuance of gender equality has embraced a longstanding sta- tistical engendering process, to r...
We discuss how latent variable models are useful to deal with the complexities of big data from diff...
One central task in machine learning (ML) is to extract hidden knowledge and structure from observed...
Latent variable modelling has gradually become an integral part of mainstream statistics and is curr...
Latent variable modelling has gradually become an integral part of mainstream statistics and is curr...
Starting from Spearman’s 1904 pioneering work on factor analysis, latent variable models have witnes...
This edited volume features cutting-edge topics from the leading researchers in the areas of latent ...
This Handbook covers latent variable models, which are a flexible class of models for modeling multi...
This article gives an overview of statistical analysis with latent variables. Us-ing traditional str...
<p>Constant technology advances have caused data explosion in recent years. Accord- ingly modern sta...
This edited volume features cutting-edge topics from the leading researchers in the areas of latent ...
Big Data offer potential benefits for statistical modelling, but confront problems including an exce...
Geography, social context, time, and cultural mindset are four (out of many) cornerstones of human i...
Many driving factors of physical systems are often latent or unobserved. Thus, understanding such sy...
Although SD modeling is sometimes called theory-rich data-poor modeling, it does not mean SD modelin...
The pursuance of gender equality has embraced a longstanding sta- tistical engendering process, to r...
We discuss how latent variable models are useful to deal with the complexities of big data from diff...
One central task in machine learning (ML) is to extract hidden knowledge and structure from observed...
Latent variable modelling has gradually become an integral part of mainstream statistics and is curr...
Latent variable modelling has gradually become an integral part of mainstream statistics and is curr...
Starting from Spearman’s 1904 pioneering work on factor analysis, latent variable models have witnes...
This edited volume features cutting-edge topics from the leading researchers in the areas of latent ...
This Handbook covers latent variable models, which are a flexible class of models for modeling multi...
This article gives an overview of statistical analysis with latent variables. Us-ing traditional str...
<p>Constant technology advances have caused data explosion in recent years. Accord- ingly modern sta...
This edited volume features cutting-edge topics from the leading researchers in the areas of latent ...
Big Data offer potential benefits for statistical modelling, but confront problems including an exce...
Geography, social context, time, and cultural mindset are four (out of many) cornerstones of human i...
Many driving factors of physical systems are often latent or unobserved. Thus, understanding such sy...
Although SD modeling is sometimes called theory-rich data-poor modeling, it does not mean SD modelin...
The pursuance of gender equality has embraced a longstanding sta- tistical engendering process, to r...
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