“how does that relate to bayesian?”
The statement “An object is potential consistencies” relates to Bayesian inference in the sense that Bayesian approaches to probability and statistical modeling also rely on the presence of underlying patterns and regularities in data.
Bayesian inference is a statistical framework that combines prior knowledge or beliefs about an event or phenomenon with new data to update the probability of the event or phenomenon. In this context, the “potential consistencies” in an object could be viewed as the prior knowledge or beliefs about its properties or behaviors.
When new data becomes available, the Bayesian approach updates the prior knowledge with the new evidence, resulting in a posterior probability that reflects the updated understanding of the object’s properties or behaviors. This process of updating beliefs based on new evidence is in line with the notion of recognizing and utilizing the potential consistencies within an object to make predictions or draw conclusions.