We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …

Besides the text content, documents and their associated words usually come with rich sets
of meta information, such as categories of documents and semantic/syntactic features of …

The authors develop an attribute-based mixed-membership model of consumers' preference
for stockkeeping units in store assortments. The model represents the underlying “topics of …

An overwhelming number of true and false news stories are posted and shared in social
networks, and users diffuse the stories based on multiple factors. Diffusion of news stories …

Topic modeling is a machine learning algorithm based on statistics that follows
unsupervised machine learning techniques for mapping a high-dimensional corpus to a low …

Besides the text content, documents usually come with rich sets of meta-information, such as
categories of documents and semantic/syntactic features of words, like those encoded in …

Statistical significance analysis, based on hypothesis tests, is a common approach for
comparing classifiers. However, many studies oversimplify this analysis by simply checking …

The intricacies inherent in contemporary real datasets demand more advanced statistical
models to effectively address complex challenges. In this article we delve into problems …

Microbiome data have been obtained relatively easily in recent years, and currently, various
methods for analyzing microbiome data are being proposed. Latent Dirichlet allocation …

We introduce a novel state space model for a set of sequentially time-stamped partial
rankings of items and textual descriptions for the items. Based on the data, the model infers …