Introducing Variational Inference in Statistics and Data Science Curriculum

Abstract

Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to their wide range of applications. In this article, we present a one-week course module for students in advanced undergraduate and applied graduate courses on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an R shiny app, and guided labs based on real data applications of logistic regression and clustering documents using Latent Dirichlet Allocation with R code. The main goal of our module is to expose students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt and adapt it to introduce more realistic case studies and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses.

KEYWORDS:

• Active learning
• Bayesian statistics
• Probabilistic models
• Statistical computing
• Variational inference

Supplementary Materials

The supplementary files for this article include the following: (a) Details of the class activity on probabilistic model for count data with variational inference; (b) The manual and the R shiny app we have developed for the module; (c) Details of the guided R logistic regression lab with U.S. women labor participation sample data; and (d) Details of the guided R lab of the LDA application to a sample of the Associated Press newspaper articles with variational inference.

Disclosure Statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Additional information

Funding

Jingchen Hu acknowledges the support from the National Science Foundation grant DUE-2215920.