Generative Modeling starts with a simple shift in perspective: instead of memorizing individual observations, learn the probabilistic structure that produced them.
If a model can represent that structure, it can use the learned distribution to evaluate data and generate new samples that follow the same underlying patterns.
That gives us the core mental model:
Learn the distribution behind the data, then use that distribution for estimation or generation.
From Observed Data to a Model Distribution
