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  1. Non-Bayesian Accounts of Evidence: Howson’s Counterexample Countered.Gordon Brittan, Mark L. Taper & Prasanta S. Bandyopadhyay - 2016 - International Studies in the Philosophy of Science 30 (3):291-298.
    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 confirmation and evidence. We demonstrate that Howson’s counterexample fails (...)
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  • Harmony and simplicity: aesthetic virtues and the rise of testability.Rhonda Martens - 2009 - Studies in History and Philosophy of Science Part A 40 (3):258-266.
    Copernicus claimed that his system was preferable in part on the grounds of its superior harmony and simplicity, but left very few hints as to what was meant by these terms. Copernicus’s pupil, Rheticus, was more forthcoming. Kepler, influenced by Rheticus, articulated further the nature of the virtues of harmony and simplicity. I argue that these terms are metaphors for the structural features of the Copernican system that make it more able to effectively exploit the available data. So it is (...)
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  • 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: 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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