Search results for 'Bayesian methods' (try it on Scholar)

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  1. Richard M. Shiffrin, Michael D. Lee, Woojae Kim & Eric‐Jan Wagenmakers (2008). A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods. Cognitive Science 32 (8):1248-1284.score: 162.0
  2. Gregor Betz (2008). Evaluating Dialectical Structures with Bayesian Methods. Synthese 163 (1):25 - 44.score: 156.0
    This paper shows how complex argumentation, analyzed as dialectical structures, can be evaluated within a Bayesian framework by interpreting them as coherence constraints on subjective degrees of belief. A dialectical structure is a set of arguments (premiss-conclusion structure) among which support- and attack-relations hold. This approach addresses the observation that some theses in a debate can be better justified than others and thus fixes a shortcoming of a theory of defeasible reasoning which applies the bivalence principle to argument evaluations (...)
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  3. John K. Kruschke (2010). What to Believe: Bayesian Methods for Data Analysis. Trends in Cognitive Sciences 14 (7):293-300.score: 150.0
  4. David Jc Mackay (1995). Bayesian Methods for Supervised Neural Networks. In Michael A. Arbib (ed.), Handbook of Brain Theory and Neural Networks. Mit Press.score: 150.0
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  5. David Barber (2002). Bayesian Methods for Supervised Neural Networks. In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. Mit Press.score: 150.0
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  6. Festa, Roberto, Optimum Inductive Methods. A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude.score: 126.0
    According to the Bayesian view, scientific hypotheses must be appraised in terms of their posterior probabilities relative to the available experimental data. Such posterior probabilities are derived from the prior probabilities of the hypotheses by applying Bayes'theorem. One of the most important problems arising within the Bayesian approach to scientific methodology is the choice of prior probabilities. Here this problem is considered in detail w.r.t. two applications of the Bayesian approach: (1) the theory of inductive probabilities (TIP) (...)
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  7. Paul Snow (1997). Nearly Bayesian Uncertain Reasoning Methods. Behavioral and Brain Sciences 20 (4):779-780.score: 126.0
    Subjects are reported as being somewhat Bayesian, but as violating the normative ideal on occasion. To abjure probability altogether is difficult. To use Bayes' Theorem scrupulously when weighing evidence can incur costs without corresponding benefits. The subjects' evident nuanced probabilism appears both realistic and reasonable.
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  8. Alexander J. Sutton, Keith R. Abrams & David R. Jones (2001). An Illustrated Guide to the Methods of Meta‐Analysis. Journal of Evaluation in Clinical Practice 7 (2):135-148.score: 90.0
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  9. Jukka Corander & Pekka Marttinen (2006). Bayesian Model Learning Based on Predictive Entropy. Journal of Logic, Language and Information 15 (1-2):5-20.score: 72.0
    Bayesian paradigm has been widely acknowledged as a coherent approach to learning putative probability model structures from a finite class of candidate models. Bayesian learning is based on measuring the predictive ability of a model in terms of the corresponding marginal data distribution, which equals the expectation of the likelihood with respect to a prior distribution for model parameters. The main controversy related to this learning method stems from the necessity of specifying proper prior distributions for all unknown (...)
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  10. Patrick Suppes (2007). Where Do Bayesian Priors Come From? Synthese 156 (3):441 - 471.score: 66.0
    Bayesian prior probabilities have an important place in probabilistic and statistical methods. In spite of this fact, the analysis of where these priors come from and how they are formed has received little attention. It is reasonable to excuse the lack, in the foundational literature, of detailed psychological theory of what are the mechanisms by which prior probabilities are formed. But it is less excusable that there is an almost total absence of a detailed discussion of the highly (...)
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  11. Daniel Barker (forthcoming). Seeing the Wood for the Trees: Philosophical Aspects of Classical, Bayesian and Likelihood Approaches in Statistical Inference and Some Implications for Phylogenetic Analysis. Biology and Philosophy:1-21.score: 66.0
    The three main approaches in statistical inference—classical statistics, Bayesian and likelihood—are in current use in phylogeny research. The three approaches are discussed and compared, with particular emphasis on theoretical properties illustrated by simple thought-experiments. The methods are problematic on axiomatic grounds (classical statistics), extra-mathematical grounds relating to the use of a prior (Bayesian inference) or practical grounds (likelihood). This essay aims to increase understanding of these limits among those with an interest in phylogeny.
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  12. Eric-Jan Wagenmakers Oliver Dyjas, Raoul P. P. P. Grasman, Ruud Wetzels, Han L. J. Van der Maas (2012). What's in a Name: A Bayesian Hierarchical Analysis of the Name-Letter Effect. Frontiers in Psychology 3.score: 66.0
    People generally prefer their initials to the other letters of the alphabet, a phenomenon known as the name-letter effect. This effect, researchers have argued, makes people move to certain cities, buy particular brands of consumer products, and choose particular professions (e.g., Angela moves to Los Angeles, Phil buys a Philips TV, and Dennis becomes a dentist). In order to establish such associations between people’s initials and their behavior, researchers typically carry out statistical analyses of large databases. Current methods of (...)
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  13. David Rindskopf (1998). Null-Hypothesis Tests Are Not Completely Stupid, but Bayesian Statistics Are Better. Behavioral and Brain Sciences 21 (2):215-216.score: 60.0
    Unfortunately, reading Chow's work is likely to leave the reader more confused than enlightened. My preferred solutions to the “controversy” about null- hypothesis testing are: (1) recognize that we really want to test the hypothesis that an effect is “small,” not null, and (2) use Bayesian methods, which are much more in keeping with the way humans naturally think than are classical statistical methods.
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  14. Nick Chater & Mike Oaksford (eds.) (2008). The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford.score: 60.0
    The rational analysis method, first proposed by John R. Anderson, has been enormously influential in helping us understand high-level cognitive processes. -/- 'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition' (OUP, 1998). It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods. It synthesizes and evaluates the progress in the past decade, taking into account developments (...)
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  15. Roger Stanev (2012). Modelling and Simulating Early Stopping of RCTs: A Case Study of Early Stop Due to Harm. Journal of Experimental and Theoretical Artificial Intelligence 24 (4):513-526.score: 60.0
    Despite efforts from regulatory agencies (e.g. NIH, FDA), recent systematic reviews of randomised controlled trials (RCTs) show that top medical journals continue to publish trials without requiring authors to report details for readers to evaluate early stopping decisions carefully. This article presents a systematic way of modelling and simulating interim monitoring decisions of RCTs. By taking an approach that is both general and rigorous, the proposed framework models and evaluates early stopping decisions of RCTs based on a clear and consistent (...)
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  16. Peter C. Austin, C. David Naylor & Jack V. Tu (2001). A Comparison of a Bayesian Vs. A Frequentist Method for Profiling Hospital Performance. Journal of Evaluation in Clinical Practice 7 (1):35-45.score: 58.0
  17. Branden Fitelson & James Hawthorne (2010). How Bayesian Confirmation Theory Handles the Paradox of the Ravens. In Ellery Eells & James Fetzer (eds.), The Place of Probability in Science. Springer. 247--275.score: 54.0
    The Paradox of the Ravens (a.k.a,, The Paradox of Confirmation) is indeed an old chestnut. A great many things have been written and said about this paradox and its implications for the logic of evidential support. The first part of this paper will provide a brief survey of the early history of the paradox. This will include the original formulation of the paradox and the early responses of Hempel, Goodman, and Quine. The second part of the paper will describe attempts (...)
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  18. Jon Williamson, From Bayesian Epistemology to Inductive Logic.score: 54.0
    Inductive logic admits a variety of semantics (Haenni et al., 2011, Part 1). This paper develops semantics based on the norms of Bayesian epistemology (Williamson, 2010, Chapter 7). §1 introduces the semantics and then, in §2, the paper explores methods for drawing inferences in the resulting logic and compares the methods of this paper with the methods of Barnett and Paris (2008). §3 then evaluates this Bayesian inductive logic in the light of four traditional critiques (...)
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  19. Stephan Hartmann, Gabriella Pigozzi & Jan Sprenger (2010). Reliable Methods of Judgment Aggregation. Journal for Logic and Computation 20:603--617.score: 54.0
    The aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on the same propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgment aggregation refers to such a problem as the \textit{discursive dilemma}. In this paper we assume that the decision which the group is trying to reach is factually right or wrong. Hence, we address the question of how good (...)
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  20. Luc Bovens & Stephan Hartmann (eds.) (2004). Bayesian Epistemology. OUP Oxford.score: 54.0
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the (...)
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  21. Ronald N. Giere (1969). Bayesian Statistics and Biased Procedures. Synthese 20 (3):371 - 387.score: 54.0
    A comparison of Neyman's theory of interval estimation with the corresponding subjective Bayesian theory of credible intervals shows that the Bayesian approach to the estimation of statistical parameters allows experimental procedures which, from the orthodox objective viewpoint, are clearly biased and clearly inadmissible. This demonstrated methodological difference focuses attention on the key difference in the two general theories, namely, that the orthodox theory is supposed to provide a known average frequency of successful estimates, whereas the Bayesian account (...)
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  22. Daniel Steel (2003). A Bayesian Way to Make Stopping Rules Matter. Erkenntnis 58 (2):213--227.score: 54.0
    Disputes between advocates of Bayesians and more orthodox approaches to statistical inference presuppose that Bayesians must regard must regard stopping rules, which play an important role in orthodox statistical methods, as evidentially irrelevant.In this essay, I show that this is not the case and that the stopping rule is evidentially relevant given some Bayesian confirmation measures that have been seriously proposed. However, I show that accepting a confirmation measure of this sort comes at the cost of rejecting two (...)
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  23. Richard Bradley (2005). Radical Probabilism and Bayesian Conditioning. Philosophy of Science 72 (2):342-364.score: 54.0
    Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but (...)
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  24. Daniel Steel (2001). Bayesian Statistics in Radiocarbon Calibration. Proceedings of the Philosophy of Science Association 2001 (3):S153-.score: 54.0
    Critics of Bayesianism often assert that scientists are not Bayesians. The widespread use of Bayesian statistics in the field of radiocarbon calibration is discussed in relation to this charge. This case study illustrates the willingness of scientists to use Bayesian statistics when the approach offers some advantage, while continuing to use orthodox methods in other contexts. The case of radiocarbon calibration, therefore, suggests a picture of statistical practice in science as eclectic and pragmatic rather than rigidly adhering (...)
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  25. Laura Fortunato, Clare Holden & Ruth Mace (2006). From Bridewealth to Dowry? Human Nature 17 (4):355-376.score: 54.0
    Significant amounts of wealth have been exchanged as part of marriage settlements throughout history. Although various models have been proposed for interpreting these practices, their development over time has not been investigated systematically. In this paper we use a Bayesian MCMC phylogenetic comparative approach to reconstruct the evolution of two forms of wealth transfers at marriage, dowry and bridewealth, for 51 Indo-European cultural groups. Results indicate that dowry is more likely to have been the ancestral practice, and that a (...)
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  26. David Corfield (2010). Varieties of Justification in Machine Learning. Minds and Machines 20 (2):291-301.score: 48.0
    Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.
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  27. Rhiannon Weaver (2008). Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT–R. Cognitive Science 32 (8):1349-1375.score: 48.0
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  28. Jean-Louis Golmard (1993). Probabilistic Inference in Artificial Intelligence: The Method of Bayesian Networks. In J. Dubucs (ed.), Philosophy of Probability. Kluwer, Dordrecht. 257--291.score: 40.0
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  29. James Provenzale (1979). Inference, Method and Decision: Towards a Bayesian Philosophy of Science. By Roger D. Rosenkranz. Modern Schoolman 56 (2):188-188.score: 40.0
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  30. Norman Fenton, Martin Neil & David A. Lagnado (2013). A General Structure for Legal Arguments About Evidence Using Bayesian Networks. Cognitive Science 37 (1):61-102.score: 38.0
    A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, (...)
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  31. Kevin Korb (2004). Bayesian Informal Logic and Fallacy. Informal Logic 24 (1).score: 38.0
    Bayesian reasoning has been applied formally to statistical inference, machine learning and analysing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments. I analyse a variety of traditional fallacies, deductive, inductive and causal, and find more merit in them than is generally acknowledged. Bayesian principles provide a framework for understanding ordinary arguments which is well worth developing.
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  32. Jeroen Keppens (2012). Argument Diagram Extraction From Evidential Bayesian Networks. Artificial Intelligence and Law 20 (2):109-143.score: 38.0
    Bayesian networks (BN) and argumentation diagrams (AD) are two predominant approaches to legal evidential reasoning, that are often treated as alternatives to one another. This paper argues that they are, instead, complimentary and proposes the beginnings of a method to employ them in such a manner. The Bayesian approach tends to be used as a means to analyse the findings of forensic scientists. As such, it constitutes a means to perform evidential reasoning. The design of Bayesian networks (...)
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  33. Julien Diard, Vincent Rynik & Jean Lorenceau (2013). A Bayesian Computational Model for Online Character Recognition and Disability Assessment During Cursive Eye Writing. Frontiers in Psychology 4.score: 38.0
    This research involves a novel apparatus, in which the user is presented with an illusion inducing visual stimulus. The user perceives illusory movement that can be followed by the eye, so that smooth pursuit eye movements can be sustained in arbitrary directions. Thus, free-flow trajectories of any shape can be traced. In other words, coupled with an eye-tracking device, this apparatus enables "eye writing", which appears to be an original object of study. We adapt a previous model of reading and (...)
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  34. Peter Spirtes (2011). Intervention, Determinism, and the Causal Minimality Condition. Synthese 182 (3):335-347.score: 36.0
    We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality condition, rather (...)
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  35. Donald Bamber (2000). Entailment with Near Surety of Scaled Assertions of High Conditional Probability. Journal of Philosophical Logic 29 (1):1-74.score: 36.0
    An assertion of high conditional probability or, more briefly, an HCP assertion is a statement of the type: The conditional probability of B given A is close to one. The goal of this paper is to construct logics of HCP assertions whose conclusions are highly likely to be correct rather than certain to be correct. Such logics would allow useful conclusions to be drawn when the premises are not strong enough to allow conclusions to be reached with certainty. This goal (...)
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  36. Michael Baumgartner & Isabelle Drouet (2013). Identifying Intervention Variables. European Journal for Philosophy of Science 3 (2):183-205.score: 36.0
    The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as so-called intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian networks (...)
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  37. Jon Williamson (2008). Objective Bayesianism with Predicate Languages. Synthese 163 (3):341 - 356.score: 36.0
    Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to first-order logical languages. It is argued that the objective Bayesian should choose a probability function, from all those that satisfy constraints imposed by background knowledge, that is closest to a particular frequency-induced probability function which generalises the λ = 0 function of Carnap’s continuum of inductive methods.
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  38. Luc Bovens & Stephan Hartmann (2003). Bayesian Epistemology. Oxford: Oxford University Press.score: 36.0
    Bovens and Hartmann provide a systematic guide to the use of probabilistic methods not just in epistemology, but also in philosophy of science, voting theory, ...
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  39. Sean Fulop & Nick Chater (2013). Editors' Introduction: Why Formal Learning Theory Matters for Cognitive Science. Topics in Cognitive Science 5 (1):3-12.score: 36.0
    This article reviews a number of different areas in the foundations of formal learning theory. After outlining the general framework for formal models of learning, the Bayesian approach to learning is summarized. This leads to a discussion of Solomonoff's Universal Prior Distribution for Bayesian learning. Gold's model of identification in the limit is also outlined. We next discuss a number of aspects of learning theory raised in contributed papers, related to both computational and representational complexity. The article concludes (...)
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  40. Steven T. Piantadosi & Edward Gibson (2014). Quantitative Standards for Absolute Linguistic Universals. Cognitive Science 38 (4):736-756.score: 36.0
    Absolute linguistic universals are often justified by cross-linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross-linguistic analysis will very rarely be able to statistically justify absolute, inviolable patterns in language. We formalize two statistical methods—frequentist and Bayesian—and show that in (...)
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  41. Nicola Angius (2014). The Problem of Justification of Empirical Hypotheses in Software Testing. Philosophy and Technology 27 (3):423-439.score: 36.0
    This paper takes part in the methodological debate concerning the nature and the justification of hypotheses about computational systems in software engineering by providing an epistemological analysis of Software Testing, the practice of observing the programs’ executions to examine whether they fulfil software requirements. Property specifications articulating such requirements are shown to involve falsifiable hypotheses about software systems that are evaluated by means of tests which are likely to falsify those hypotheses. Software Reliability metrics, used to measure the growth of (...)
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  42. A. N. Golodnikov, P. S. Knopov & V. A. Pepelyaev (2004). Estimation of Reliability Parameters Under Incomplete Primary Information. Theory and Decision 57 (4):331-344.score: 36.0
    We consider the procedure for small-sample estimation of reliability parameters. The main shortcomings of the classical methods and the Bayesian approach are analyzed. Models that find robust Bayesian estimates are proposed. The sensitivity of the Bayesian estimates to the choice of the prior distribution functions is investigated using models that find upper and lower bounds. The proposed models reduce to optimization problems in the space of distribution functions.
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  43. Thomas A. Weber (2010). Simple Methods for Evaluating and Comparing Binary Experiments. Theory and Decision 69 (2):257-288.score: 36.0
    We consider a confidence parametrization of binary information sources in terms of appropriate likelihood ratios. This parametrization is used for Bayesian belief updates and for the equivalent comparison of binary experiments. In contrast to the standard parametrization of a binary information source in terms of its specificity and its sensitivity, one of the two confidence parameters is sufficient for a Bayesian belief update conditional on a signal realization. We introduce a confidence-augmented receiver operating characteristic for comparisons of binary (...)
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  44. M. C. Bradley (2002). The Fine-Tuning Argument: The Bayesian Version. Religious Studies 38 (4):375-404.score: 30.0
    This paper considers the Bayesian form of the fine-tuning argument as advanced by Richard Swinburne. An expository section aims to identify the precise character of the argument, and three lines of objection are then advanced. The first of these holds that there is an inconsistency in Swinburne's procedure, the second that his argument has an unacceptable dependence on an objectivist theory of value, the third that his method is powerless to single out traditional theism from a vast number (...)
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  45. James Hawthorne (1993). Bayesian Induction IS Eliminative Induction. Philosophical Topics 21 (1):99-138.score: 30.0
    Eliminative induction is a method for finding the truth by using evidence to eliminate false competitors. It is often characterized as "induction by means of deduction"; the accumulating evidence eliminates false hypotheses by logically contradicting them, while the true hypothesis logically entails the evidence, or at least remains logically consistent with it. If enough evidence is available to eliminate all but the most implausible competitors of a hypothesis, then (and only then) will the hypothesis become highly confirmed. I will argue (...)
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  46. Joel D. Velasco (2008). The Prior Probabilities of Phylogenetic Trees. Biology and Philosophy 23 (4):455-473.score: 30.0
    Bayesian methods have become among the most popular methods in phylogenetics, but theoretical opposition to this methodology remains. After providing an introduction to Bayesian theory in this context, I attempt to tackle the problem mentioned most often in the literature: the “problem of the priors”—how to assign prior probabilities to tree hypotheses. I first argue that a recent objection—that an appropriate assignment of priors is impossible—is based on a misunderstanding of what ignorance and bias are. I (...)
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  47. Bengt Autzen (2011). Constraining Prior Probabilities of Phylogenetic Trees. Biology and Philosophy 26 (4):567-581.score: 30.0
    Although Bayesian methods are widely used in phylogenetic systematics today, the foundations of this methodology are still debated among both biologists and philosophers. The Bayesian approach to phylogenetic inference requires the assignment of prior probabilities to phylogenetic trees. As in other applications of Bayesian epistemology, the question of whether there is an objective way to assign these prior probabilities is a contested issue. This paper discusses the strategy of constraining the prior probabilities of phylogenetic trees by (...)
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  48. Keith Stenning & Michiel van Lambalgen (2009). “Nonmonotonic” Does Not Mean “Probabilistic”. Behavioral and Brain Sciences 32 (1):102-103.score: 30.0
    Oaksford & Chater (O&C) advocate Bayesian probability as a way to deal formally with the pervasive nonmonotonicity of common sense reasoning. We show that some forms of nonmonotonicity cannot be treated by Bayesian methods.
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  49. Cory Juhl (1993). Bayesianism and Reliable Scientific Inquiry. Philosophy of Science 60 (2):302-319.score: 30.0
    The inductive reliability of Bayesian methods is explored. The first result presented shows that for any solvable inductive problem of a general type, there exists a subjective prior which yields a Bayesian inductive method that solves the problem, although not all subjective priors give rise to a successful inductive method for the problem. The second result shows that the same does not hold for computationally bounded agents, so that Bayesianism is "inductively incomplete" for such agents. Finally a (...)
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