probabilistic reasoning

Probabilistic reasoning is a human-made method of drawing conclusions under uncertainty by assigning and manipulating numerical degrees of belief (probabilities) according to the axioms of probability theory. Its parameters are: (1) a formal probability space with sample space, events, and a probability measure; (2) inference rules that propagate uncertainty through Bayes' theorem, marginalization, or other probabilistic operations; and (3) a decision criterion that maps posterior beliefs to actions. It persists through teaching in mathematics and statistics curricula, publication in academic literature, and implementation in computational systems. [formal: probabilisaticus | substrate: mind | horizon: hours | explicit: yes | epoch: 0.01]