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Justifying the Norms of Inductive Inference

Vassend, Olav Benjamin (2019) Justifying the Norms of Inductive Inference. [Preprint]

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Abstract

Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none of the hypotheses under consideration is true and because it is committed to always using the likelihood as a measure of evidential favoring, even when that is inappropriate. The purpose of this paper is to study inductive inference in a very general setting where finding the truth is not necessarily the goal and where the measure of evidential favoring is not necessarily the likelihood. I use an accuracy argument to argue for probabilism and I develop a new kind of argument to argue for two general updating rules, both of which are reasonable in different contexts. One of the updating rules has standard Bayesian updating, Bissiri et al.'s (2016) general Bayesian updating, and Vassend's (2019b) quasi-Bayesian updating as special cases. The other updating rule is novel.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Vassend, Olav Benjaminvassend@ntu.edu.sg
Keywords: Inductive inference Bayesian inference Bayesianism Statistical inference Philosophy of statistics General philosophy of science Conditionalization
Subjects: General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Depositing User: Olav Vassend
Date Deposited: 21 Feb 2019 14:29
Last Modified: 21 Feb 2019 14:29
Item ID: 15750
Subjects: General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Date: 19 February 2019
URI: http://philsci-archive.pitt.edu/id/eprint/15750

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