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  1. Two dogmas of strong objective bayesianism.Prasanta S. Bandyopadhyay & Gordon Brittan - 2010 - International Studies in the Philosophy of Science 24 (1):45 – 65.
    We introduce a distinction, unnoticed in the literature, between four varieties of objective Bayesianism. What we call ' strong objective Bayesianism' is characterized by two claims, that all scientific inference is 'logical' and that, given the same background information two agents will ascribe a unique probability to their priors. We think that neither of these claims can be sustained; in this sense, they are 'dogmatic'. The first fails to recognize that some scientific inference, in particular that concerning evidential relations, is (...)
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  • How to Undermine Underdetermination?Prasanta S. Bandyopadhyay, John G. Bennett & Megan D. Higgs - 2015 - Foundations of Science 20 (2):107-127.
    The underdetermination thesis poses a threat to rational choice of scientific theories. We discuss two arguments for the thesis. One draws its strength from deductivism together with the existence thesis, and the other is defended on the basis of the failure of a reliable inductive method. We adopt a partially subjective/objective pragmatic Bayesian epistemology of science framework, and reject both arguments for the thesis. Thus, in science we are able to reinstate rational choice called into question by the underdetermination thesis.
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  • Empiricism and/or Instrumentalism?Prasanta S. Bandyopadhyay, Mark Greenwood, Gordon Brittan & Ken A. Aho - 2014 - Erkenntnis 79 (5):1019-1041.
    Elliott Sober is both an empiricist and an instrumentalist. His empiricism rests on a principle called actualism, whereas his instrumentalism violates this. This violation generates a tension in his work. We argue that Sober is committed to a conflicting methodological imperative because of this tension. Our argument illuminates the contemporary debate between realism and empiricism which is increasingly focused on the application of scientific inference to testing scientific theories. Sober’s position illustrates how the principle of actualism drives a wedge between (...)
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  • The role of Bayesian philosophy within Bayesian model selection.Jan Sprenger - 2013 - European Journal for Philosophy of Science 3 (1):101-114.
    Bayesian model selection has frequently been the focus of philosophical inquiry (e.g., Forster, Br J Philos Sci 46:399–424, 1995; Bandyopadhyay and Boik, Philos Sci 66:S390–S402, 1999; Dowe et al., Br J Philos Sci 58:709–754, 2007). This paper argues that Bayesian model selection procedures are very diverse in their inferential target and their justification, and substantiates this claim by means of case studies on three selected procedures: MML, BIC and DIC. Hence, there is no tight link between Bayesian model selection and (...)
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  • The curve-fitting problem: An objectivist view.Stanley A. Mulaik - 2001 - Philosophy of Science 68 (2):218-241.
    Model simplicity in curve fitting is the fewness of parameters estimated. I use a vector model of least squares estimation to show that degrees of freedom, the difference between the number of observed parameters fit by the model and the number of explanatory parameters estimated, are the number of potential dimensions in which data are free to differ from a model and indicate the disconfirmability of the model. Though often thought to control for parameter estimation, the AIC and similar indices (...)
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  • (1 other version)Statistical Model Selection Criteria and Bayesianism.I. A. Kieseppä - 2001 - Philosophy of Science 68 (S3):S141-S152.
    Two Bayesian approaches to choosing between statistical models are contrasted. One of these is an approach which Bayesian statisticians regularly use for motivating the use of AIC, BIC, and other similar model selection criteria, and the other one is a new approach which has recently been proposed by Bandyopadhayay, Boik, and Basu. The latter approach is criticized, and the basic ideas of the former approach are presented in a way that makes them accessible to a philosophical audience. It is also (...)
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  • Are the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC) Applicable in Determining the Optimal Fit and Simplicity of Mechanistic Models?Jens Harbecke, Jonas Grunau & Philip Samanek - 2024 - International Studies in the Philosophy of Science 37 (1):17-36.
    Over the past three decades, the discourse on the mechanistic approach to scientific modelling and explanation has notably sidestepped the topic of simplicity and fit within the process of model selection. This paper aims to rectify this disconnect by delving into the topic of simplicity and fit within the context of mechanistic explanations. More precisely, our primary objective is to address whether simplicity metrics hold any significance within mechanistic explanations. If they do, then our inquiry extends to the suitability of (...)
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  • The curve fitting problem: A bayesian rejoinder.Prasanta S. Bandyopadhyay & Robert J. Boik - 1999 - Philosophy of Science 66 (3):402.
    In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit pull in opposite directions. To solve this problem, two proposals, the first one based on Bayes's theorem criterion (BTC) and the second one advocated by Forster and Sober based on Akaike's Information Criterion (AIC) are discussed. We show that AIC, which is frequentist in spirit, is logically equivalent to BTC, provided that a suitable choice of priors is made. We evaluate the charges against Bayesianism and contend that AIC approach (...)
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