Bayesian Theories Sample Clauses

Bayesian Theories. ‌ Xxxxxxxx approaches to vagueness have numerous possible entry points into the literature. Proposed Bayesian accounts can be found as daughters in all three subdivisions of the tree in §1.3 (daughters of [11], [10] and [0]). However, one could also imagine the integration of probability calculus into other established theories. For example, a probabilistic contextualism ([1111] or [1110]). Below is a brief description of some Bayesian theories. Augmented Bayesian Theories Probability calculus, developed in Kolmogorov (1950), is meant to model reasoning in conditions of uncertainty. In (Xxxxxxxx 2011), the Bayesian machinery is been applied to model uncertainties over the extensions of a vague term (propositions) when used in an utterance (Bayesian Epistemic Logic [1000]). These approaches tend to assume that there is are multiple possible interpretations of vague terms. Probability calculus is then used to provide some structure over these possible interpretations such that some are more probable than others. These augmented theories tend to inherit a semantics from an alternative approach. The probabilistic aspect to these approaches provides a modelling of the uncertainty we seem to have over what the extensions of vague terms are. Xxxxxxxxx’x Pure Degree Theory Xxxxxxxxx’x pure degree theory (located at [110]) is the only one not to be an [0] daughter. I take this position to be Xxxxxxxxx’x for two reasons: (i) Xxxxxxxxx defends bivalence45. That rules out a fuzzy-like interpretation.
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