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Confirmation and Evidence DistinguishedIn Mark Taper, Gordon Brittan & Prasanta Bandyopadhyay (eds.), Belief, Evidence, and Uncertainty: Problems of Epistemic Inference, Springer Verlag. 2016.
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Initial Difficulties DispelledIn Mark Taper, Gordon Brittan & Prasanta Bandyopadhyay (eds.), Belief, Evidence, and Uncertainty: Problems of Epistemic Inference, Springer Verlag. 2016.
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Veridical and Misleading EvidenceIn Mark Taper, Gordon Brittan & Prasanta Bandyopadhyay (eds.), Belief, Evidence, and Uncertainty: Problems of Epistemic Inference, Springer Verlag. 2016.
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Concluding ReflectionsIn Mark Taper, Gordon Brittan & Prasanta Bandyopadhyay (eds.), Belief, Evidence, and Uncertainty: Problems of Epistemic Inference, Springer Verlag. 2016.
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A Subjective Bayesian Surrogate for EvidenceIn Mark Taper, Gordon Brittan & Prasanta Bandyopadhyay (eds.), Belief, Evidence, and Uncertainty: Problems of Epistemic Inference, Springer Verlag. 2016.
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Bayesian and Evidential ParadigmsIn Mark Taper, Gordon Brittan & Prasanta Bandyopadhyay (eds.), Belief, Evidence, and Uncertainty: Problems of Epistemic Inference, Springer Verlag. 2016.
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35Non-Bayesian Accounts of Evidence: Howson’s Counterexample CounteredInternational Studies in the Philosophy of Science 30 (3): 291-298. 2016.There is a debate in Bayesian confirmation theory between subjective and non-subjective accounts of evidence. Colin Howson has provided a counterexample to our non-subjective account of evidence: the counterexample refers to a case in which there is strong evidence for a hypothesis, but the hypothesis is highly implausible. In this article, we contend that, by supposing that strong evidence for a hypothesis makes the hypothesis more believable, Howson conflates the distinction between confirmati…Read more
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46Model structure adequacy analysis: selecting models on the basis of their ability to answer scientific questionsSynthese 163 (3): 357-370. 2008.Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as a means…Read more
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405The major competing statistical paradigms share a common remarkable but unremarked thread: in many of their inferential applications, different probability interpretations are combined. How this plays out in different theories of inference depends on the type of question asked. We distinguish four question types: confirmation, evidence, decision, and prediction. We show that Bayesian confirmation theory mixes what are intuitively “subjective” and “objective” interpretations of probability, where…Read more
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43Belief, Evidence, and Uncertainty: Problems of Epistemic InferenceSpringer Verlag. 2016.It can be demonstrated in a very straightforward way that confirmation and evidence as spelled out by us can vary from one case to the next, that is, a hypothesis may be weakly confirmed and yet the evidence for it can be strong, and conversely, the evidence may be weak and the confirmation strong. At first glance, this seems puzzling; the puzzlement disappears once it is understood that confirmation is of single hypotheses, in which there is an initial degree of belief which is adjusted up or d…Read more
Bozeman, Montana, United States of America
Areas of Specialization
Other Academic Areas |
Science, Logic, and Mathematics |
Philosophy of Statistics |
Areas of Interest
Other Academic Areas |
Science, Logic, and Mathematics |
Philosophy of Statistics |