Data continuously stream into our minds, guiding our learn-
ing and inference with no trial delimiters to parse our experi-
ence. These data can take on a variety of forms, but research
on causal learning has emphasized discrete contingency data
over continuous sequences of events. We present a formal
framework for modeling causal inferences about sequences
of point events, based on Bayesian inference over nonhomo-
geneous Poisson processes (NHPPs). We show how to apply
this framework to successfully model data from an experiment
by Lagnado and Speekenbrink (2010) which examined human
learning from sequences of point events.
