ABSTRACT
This chapter begins to build Bayesian models. It focuses on the small world. The chapter explains probability theory in its essential form: counting the ways things can happen. It presents the stylized components of a Bayesian statistical model, a model for learning from data. The chapter shows how to animate the model, to produce estimates. The way that Bayesian models learn from evidence is arguably optimal in the small world. Cristoforo Colombo made a prediction based upon his view that the world was small. A Bayesian model begins with one set of plausibilities assigned to each of these possibilities. The Bayesian model learns in a way that is demonstrably optimal, provided that it accurately describes the real, large world.
