  We describe a technique to learn the underlying structure of collider events
directly from the data, without having a particular theoretical model in mind.
It allows to infer aspects of the theoretical model that may have given rise to
this structure, and can be used to cluster or classify the events for analysis
purposes. The unsupervised machine-learning technique is based on the
probabilistic (Bayesian) generative model of Latent Dirichlet Allocation. We
pair the model with an approximate inference algorithm called Variational
Inference, which we then use to extract the latent probability distributions
describing the learned underlying structure of collider events. We provide a
detailed systematic study of the technique using two example scenarios to learn
the latent structure of di-jet event samples made up of QCD background events
and either $t\bar{t}$ or hypothetical $W' \to (\phi \to WW) W$ signal events.
