Discover Hidden Relationships in Your Data with Latent Variable Modeling

Like currents in an ocean, unobservable phenomena often have observable consequences. Image generated with Leonardo AI

Have you ever noticed that some datasets just don’t behave as expected? You tweak a model, adjust variables, and still, you’re just not getting the results you thought you’d be getting.

This can happen when some of the most important patterns in your dataset are hidden. Until you use latent variable modeling (LVM) — then, suddenly, everything makes sense.

LVM allows us to capture unobserved influences. We do not always have complete data for the real factors driving stock prices, the hidden dimensions behind customer behavior, or the unspoken climate risks that standard models miss. That doesn’t make these factors less real, though, and as data scientists we need to account for them.

Latent variable modeling is a technique to do just that. It helps us improve predictions, detect anomalies, and extract deeper insights from data.

We’ll go through some of the foundations of LVM, discuss some real-world applications of it, and give some hands-on coding examples so you can get a feeling for it. Let’s get into it!

Foundations of Latent Variables

Everyday Intuition for Latent Variables