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Nonstandard Bayesianism: How Verisimilitude and Counterfactual Degrees of Belief Solve the Interpretive Problem in Bayesian Inference

Vassend, Olav B. (2017) Nonstandard Bayesianism: How Verisimilitude and Counterfactual Degrees of Belief Solve the Interpretive Problem in Bayesian Inference. In: UNSPECIFIED.

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Abstract

Scientists and Bayesian statisticians often study hypotheses that they know to be false. This creates an interpretive problem because the Bayesian probability of a hypothesis is typically interpreted as a degree of belief that the hypothesis is true. In this paper, I present and contrast two solutions to the interpretive problem, both of which involve reinterpreting the Bayesian framework in such a way that pragmatic factors directly determine in part how probability assignments are interpreted and whether a given probability assignment is rational. I argue that there is an important sense in which the two solutions are equivalent, and I suggest that the two reinterpretations can help us do Bayesian inference better. I also explore various features of the two reinterprations, including their relations to the standard Bayesian interpretation of probability and to the Law of Likelihood.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Vassend, Olav B.vassend@ntu.edu.sg
Keywords: Bayesian statistics, verisimilitude, closeness to the truth, interpretation of probability, Bayesian inference, counterfactual degrees of belief
Subjects: General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
Depositing User: Olav Vassend
Date Deposited: 04 Aug 2017 15:17
Last Modified: 04 Aug 2017 15:17
Item ID: 13301
Subjects: General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
Date: 4 August 2017
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/13301

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