Latent Variable Models

–Graphical Model Comparison–

In the era of big data, high-dimensional graphical models have become a standard tool for inferring and understanding conditional independence relationships between variables in high-dimensional random vectors. When the data becomes extremely high dimensional relative to the sample size, it may become difficult to interpret, and even to detect, the conditional independence relationships between components in these vectors. In practice, it is common to perform an initial dimension reduction step to overcome these issues.

Goals of this project include dimension reduction by clustering, estimation of latent variable models and a framework for post-dimension reduction inference.


  • GFORCE: R Package on CRAN implementing the methods developed in this project.
  • R_GFORCE: Development branch of GFORCE package.
Carson Eisenach
Carson Eisenach
Senior Applied Scientist, Amazon

My research interests include optimization and machine learning.