Bayesianism and Information
نویسنده
چکیده
Bayesianism is a theory of inductive inference that makes use of the mathematical theory of probability. Bayesians usually hold that the relevant probabilities should be interpreted in terms of rational degrees of belief. This still leaves much scope for disagreement, since there is no consensus about what norms govern rational degrees of belief. In this chapter, we first provide an introduction to three Bayesian theories that adopt a degree of belief interpretation of probability: (i) strictly subjective Bayesianism, (ii) empirically based subjective Bayesianism, and (iii) objective Bayesianism. Then we discuss how one might appeal to information theory in order to justify the norms of objective Bayesianism.
منابع مشابه
Bayesianism and Language Change
Bayesian probability is normally defined over a fixed language or event space. But in practice language is susceptible to change, and the question naturally arises as to how Bayesian degrees of belief should change as language changes. I argue here that this question poses a serious challenge to Bayesianism. The Bayesian may be able to meet this challenge however, and I outline a practical meth...
متن کاملBayesianism without learning
According to the standard definition, a Bayesian agent is one who forms his posterior belief by conditioning his prior belief on what he has learned, that is, on facts of which he has become certain. Here it is shown that Bayesianism can be described without assuming that the agent acquires any certain information; an agent is Bayesian if his prior, when conditioned on his posterior belief, agr...
متن کاملLocating IBE in the Bayesian framework
Inference to the Best Explanation (IBE) and Bayesianism are our two most prominent theories of scientific inference. Are they compatible? Van Fraassen famously argued that they are not, concluding that IBE must be wrong since Bayesianism is right. Writers since then, from both the Bayesian and explanationist camps, have usually considered van Fraassen’s argument to be misguided, and have plumpe...
متن کاملProbabilistic Sophistication and Reverse Bayesianism
This paper extends our earlier work on reverse Bayesianism by relaxing the assumption that decision makers abide by expected utility theory, assuming instead weaker axioms that merely imply that they are probabilistically sophisticated. We show that our main results, namely, (modified) representation theorems and corresponding rules for updating beliefs over expanding state spaces and null even...
متن کاملOn Nonparametric Predictive Inference and Objective Bayesianism
This paper consists of three main parts. First, we give an introduction to Hill’s assumption A(n) and to theory of interval probability, and an overview of recently developed theory and methods for nonparametric predictive inference (NPI), which is based on A(n) and uses interval probability to quantify uncertainty. Thereafter, we illustrate NPI by introducing a variation to the assumption A(n)...
متن کامل