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Foundations of Bayesian Instrumentalism.
~
Vassend, Olav B.
Foundations of Bayesian Instrumentalism.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Foundations of Bayesian Instrumentalism./
作者:
Vassend, Olav B.
面頁冊數:
1 online resource (167 pages)
附註:
Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
標題:
Philosophy. -
電子資源:
click for full text (PQDT)
ISBN:
9781369753042
Foundations of Bayesian Instrumentalism.
Vassend, Olav B.
Foundations of Bayesian Instrumentalism.
- 1 online resource (167 pages)
Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.
Includes bibliographical references
According to Bayesian epistemologists and statisticians, plausibility judgments are representable by real numbers, and these numbers ought, rationally, to obey the probability axioms. For example, if you find P very plausible, then it is irrational for you to also find the negation of P very plausible. More generally, you can use the Bayesian framework to decide which of your plausibility judgments are rational and how your judgments ought to change given evidence. Bayesianism is not the only such framework, but it is arguably the most influential one.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781369753042Subjects--Topical Terms:
559771
Philosophy.
Index Terms--Genre/Form:
554714
Electronic books.
Foundations of Bayesian Instrumentalism.
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Source: Dissertation Abstracts International, Volume: 78-09(E), Section: A.
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Adviser: Malcolm Forster.
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Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.
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According to Bayesian epistemologists and statisticians, plausibility judgments are representable by real numbers, and these numbers ought, rationally, to obey the probability axioms. For example, if you find P very plausible, then it is irrational for you to also find the negation of P very plausible. More generally, you can use the Bayesian framework to decide which of your plausibility judgments are rational and how your judgments ought to change given evidence. Bayesianism is not the only such framework, but it is arguably the most influential one.
520
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There are good reasons for taking the Bayesian framework seriously. On the one hand, multiple independent arguments purport to show that any numerical plausibility measure ought to be a probability distribution. On the other hand, and perhaps more importantly, the Bayesian framework has been applied very successfully both within philosophy and within statistics.
520
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However, Bayesianism faces multiple objections. For example, Bayesians typically interpret the probability of a hypothesis as the plausibility that the hypothesis is true, which suggests that scientific inference is a search for true hypotheses; however, scientists often use Bayesian methods to explore scientific models they already know to be false. Moreover, the status of Bayesian rational norms is unclear, because ordinary human beings typically do not have epistemic attitudes as fine-grained as the Bayesian framework seems to assume.
520
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The major goal of my dissertation is to argue that the norms that govern Bayesian quantities, such as an agent's probability function or confirmation measure, are systematically influenced by the goals of the relevant agent. In other words, Bayesianism underwrites a kind of epistemic instrumentalism. In particular, I will argue that the fact that Bayesian norms are goal-relative implies that the Bayesian framework does not make unrealistic assumptions about human psychology. Moreover, I will show that Bayesians are not committed to interpreting probability distributions as representing plausibility judgments, and that in fact some goals require that we interpret the Bayesian probability of a hypothesis as something other than the plausibility that the hypothesis is true.
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Ann Arbor, Mich. :
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2018
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Mode of access: World Wide Web
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click for full text (PQDT)
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