Postconditionalization

Postconditionalization refers to a theoretical concept within Bayesian statistics and related fields. It describes a process where updated beliefs or probabilities are formed *after* the observation of evidence, but in a way that diverges from standard Bayesian updating. Instead of directly calculating the posterior probability using Bayes' theorem, postconditionalization might involve a heuristic, an approximation, or a modification of the standard procedure. This can occur in situations where direct application of Bayes' theorem is computationally intractable, or when there are practical reasons to adjust the updating mechanism.

The term "postconditionalization" highlights that the conditioning on evidence happens, but not necessarily in the most straightforward or theoretically pure Bayesian manner. It can sometimes be used to describe scenarios where a system or agent revises its beliefs based on new information, but the mechanism of revision is not a strict application of the mathematical rules of probability. This might involve subjective adjustments or reliance on simplified models. While standard Bayesian updating is a well-defined mathematical operation, postconditionalization encompasses a broader set of belief revision strategies that occur post-observation. Understanding this distinction is important in fields like artificial intelligence, machine learning, and decision theory where agents need to adapt their knowledge based on incoming data.