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  1. Good Thinking: The Foundations of Probability and its Applications.Irving John Good - 1983 - Univ Minnesota Pr.
    ... Press for their editorial perspicacity, to the National Institutes of Health for the partial financial support they gave me while I was writing some of the chapters, and to Donald Michie for suggesting the title Good Thinking.
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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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  • Scientific reasoning: the Bayesian approach.Peter Urbach & Colin Howson - 1993 - Chicago: Open Court. Edited by Peter Urbach.
    Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...
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  • Measuring confirmation.David Christensen - 1999 - Journal of Philosophy 96 (9):437-461.
    The old evidence problem affects any probabilistic confirmation measure based on comparing pr(H/E) and pr(H). The article argues for the following points: (1) measures based on likelihood ratios also suffer old evidence difficulties; (2) the less-discussed synchronic old evidence problem is, in an important sense, the most acute; (3) prominent attempts to solve or dissolve the synchronic problem fail; (4) a little-discussed variant of the standard measure avoids the problem, in an appealing way; and (5) this measure nevertheless reveals a (...)
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  • Belief, Evidence, and Uncertainty: Problems of Epistemic Inference.Mark Taper, Gordon Brittan & Prasanta Bandyopadhyay - 2016 - Cham, Switzerland: Springer Verlag. Edited by Gordon Brittan Jr & Mark L. Taper.
    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 (...)
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  • The Foundations of Causal Decision Theory.James M. Joyce - 1999 - Cambridge University Press.
    This book defends the view that any adequate account of rational decision making must take a decision maker's beliefs about causal relations into account. The early chapters of the book introduce the non-specialist to the rudiments of expected utility theory. The major technical advance offered by the book is a 'representation theorem' that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory. The book solves (...)
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  • (2 other versions)Error and the Growth of Experimental Knowledge.Deborah Mayo - 1997 - British Journal for the Philosophy of Science 48 (3):455-459.
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  • The foundations of causal decision theory. [REVIEW]Mirek Janusz - 2001 - Philosophical Review 110 (2):296-300.
    This book makes a significant contribution to the standard decision theory, that is, the theory of choice built around the principle of maximizing expected utility, both to its causal version and to the more traditional noncausal approach. The author’s success in clarifying the foundations of the standard decision theory in general, and causal decision theory in particular, also makes the book uniquely suitable for a person whose research in philosophy has led her to want to learn about contemporary decision theory. (...)
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  • Exhuming the No-Miracles Argument.Colin Howson - 2013 - Analysis 73 (2):205-211.
    The No-Miracles Argument has a natural representation as a probabilistic argument. As such, it commits the base-rate fallacy. In this article, I argue that a recent attempt to show that there is still a serviceable version that avoids the base-rate fallacy fails, and with it all realistic hope of resuscitating the argument.
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  • (2 other versions)Error and the growth of experimental knowledge.Deborah Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
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  • Error and the Growth of Experimental Knowledge.Deborah G. Mayo - 1996 - University of Chicago.
    This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.
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  • (1 other version)The Curve Fitting Problem: A Bayesian Approach.Prasanta S. Bandyopadhayay, Robert J. Boik & Prasun Basu - 1996 - Philosophy of Science 63 (5):S264-S272.
    In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit, pull in opposite directions. To this problem, we propose a solution that strikes a balance between simplicity and goodness-of-fit. Using Bayes' theorem we argue that the notion of prior probability represents a measurement of simplicity of a theory, whereas the notion of likelihood represents the theory's goodness-of-fit. We justify the use of prior probability and show how to calculate the likelihood of a family of curves. We diagnose the relationship (...)
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