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: 51.0
  2. Gregor Betz (2008). Evaluating Dialectical Structures with Bayesian Methods. Synthese 163 (1):25 - 44.score: 48.0
    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. David Barber (2002). Bayesian Methods for Supervised Neural Networks. In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. Mit Press.score: 45.0
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  4. John K. Kruschke (2010). What to Believe: Bayesian Methods for Data Analysis. Trends in Cognitive Sciences 14 (7):293-300.score: 45.0
  5. 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: 45.0
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  6. Otso Ovaskainen, Hanna Rekola, Evgeniy Meyke & Elja Arjas (2008). Bayesian Methods for Analyzing Movements in Heterogeneous Landscapes From Mark-Recapture Data. In Carolyn Merchant (ed.), Ecology. Humanity Books. 542-554.score: 45.0
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  7. Festa, Roberto, Optimum Inductive Methods. A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude.score: 39.0
    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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  8. Paul Snow (1997). Nearly Bayesian Uncertain Reasoning Methods. Behavioral and Brain Sciences 20 (4):779-780.score: 39.0
    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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  9. 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: 39.0
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  10. 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: 30.0
    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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  11. Jukka Corander & Pekka Marttinen (2006). Bayesian Model Learning Based on Predictive Entropy. Journal of Logic, Language and Information 15 (1-2):5-20.score: 30.0
    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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  12. Patrick Suppes (2007). Where Do Bayesian Priors Come From? Synthese 156 (3):441 - 471.score: 27.0
    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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  13. Laura Fortunato, Clare Holden & Ruth Mace (2006). From Bridewealth to Dowry? Human Nature 17 (4):355-376.score: 27.0
    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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  14. 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: 27.0
    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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  15. David Rindskopf (1998). Null-Hypothesis Tests Are Not Completely Stupid, but Bayesian Statistics Are Better. Behavioral and Brain Sciences 21 (2):215-216.score: 24.0
    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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  16. David Corfield (2010). Varieties of Justification in Machine Learning. Minds and Machines 20 (2):291-301.score: 24.0
    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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  17. Nick Chater & Mike Oaksford (eds.) (2008). The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford.score: 24.0
    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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  18. 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: 24.0
  19. Rhiannon Weaver (2008). Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT–R. Cognitive Science 32 (8):1349-1375.score: 24.0
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  20. 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: 21.0
    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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  21. Jon Williamson, From Bayesian Epistemology to Inductive Logic.score: 21.0
    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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  22. Luc Bovens & Stephan Hartmann (eds.) (2004). Bayesian Epistemology. OUP Oxford.score: 21.0
    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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  23. Stephan Hartmann, Gabriella Pigozzi & Jan Sprenger (2010). Reliable Methods of Judgment Aggregation. Journal for Logic and Computation 20:603--617.score: 21.0
    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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  24. Ronald N. Giere (1969). Bayesian Statistics and Biased Procedures. Synthese 20 (3):371 - 387.score: 21.0
    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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  25. Daniel Steel (2003). A Bayesian Way to Make Stopping Rules Matter. Erkenntnis 58 (2):213--227.score: 21.0
    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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  26. 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: 21.0
    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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  27. Jeroen Keppens (2012). Argument Diagram Extraction From Evidential Bayesian Networks. Artificial Intelligence and Law 20 (2):109-143.score: 21.0
    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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  28. Daniel Steel (2001). Bayesian Statistics in Radiocarbon Calibration. Proceedings of the Philosophy of Science Association 2001 (3):S153-.score: 21.0
    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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  29. 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: 21.0
    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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  30. Kevin Korb (2004). Bayesian Informal Logic and Fallacy. Informal Logic 24 (1).score: 21.0
    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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  31. Patrick Maher (2010). Bayesian Probability. Synthese 172 (1):119 - 127.score: 18.0
    Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation makes Bayesian decision theory a poor explication of the relevant concept of rational choice. A satisfactory conception of Bayesian decision theory is obtained by taking Bayesian probability to be an explicatum for (...)
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  32. Geoff Pynn (2013). The Bayesian Explanation of Transmission Failure. Synthese 190 (9):1519-1531.score: 18.0
    Even if our justified beliefs are closed under known entailment, there may still be instances of transmission failure. Transmission failure occurs when P entails Q, but a subject cannot acquire a justified belief that Q by deducing it from P. Paradigm cases of transmission failure involve inferences from mundane beliefs (e.g., that the wall in front of you is red) to the denials of skeptical hypotheses relative to those beliefs (e.g., that the wall in front of you is not white (...)
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  33. Jon Williamson (2011). Objective Bayesianism, Bayesian Conditionalisation and Voluntarism. Synthese 178 (1):67-85.score: 18.0
    Objective Bayesianism has been criticised on the grounds that objective Bayesian updating, which on a finite outcome space appeals to the maximum entropy principle, differs from Bayesian conditionalisation. The main task of this paper is to show that this objection backfires: the difference between the two forms of updating reflects negatively on Bayesian conditionalisation rather than on objective Bayesian updating. The paper also reviews some existing criticisms and justifications of conditionalisation, arguing in particular that the diachronic (...)
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  34. Peter Spirtes (2011). Intervention, Determinism, and the Causal Minimality Condition. Synthese 182 (3):335-347.score: 18.0
    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. Matthias Unterhuber & Gerhard Schurz (2013). The New Tweety Puzzle: Arguments Against Monistic Bayesian Approaches in Epistemology and Cognitive Science. Synthese 190 (8):1407-1435.score: 18.0
    In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...)
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  36. Terry Horgan (2008). Synchronic Bayesian Updating and the Sleeping Beauty Problem: Reply to Pust. Synthese 160 (2):155 - 159.score: 18.0
    I maintain, in defending “thirdism,” that Sleeping Beauty should do Bayesian updating after assigning the “preliminary probability” 1/4 to the statement S: “Today is Tuesday and the coin flip is heads.” (This preliminary probability obtains relative to a specific proper subset I of her available information.) Pust objects that her preliminary probability for S is really zero, because she could not be in an epistemic situation in which S is true. I reply that the impossibility of being in such (...)
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  37. Richard Bradley (2007). A Unified Bayesian Decision Theory. Theory and Decision 63 (3):233-263,.score: 18.0
    This paper provides new foundations for Bayesian Decision Theory based on a representation theorem for preferences defined on a set of prospects containing both factual and conditional possibilities. This use of a rich set of prospects not only provides a framework within which the main theoretical claims of Savage, Ramsey, Jeffrey and others can be stated and compared, but also allows for the postulation of an extended Bayesian model of rational belief and desire from which they can be (...)
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  38. Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann (2011). Confirmation and Reduction: A Bayesian Account. Synthese 179 (2):321 - 338.score: 18.0
    Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each (...)
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  39. Frederick Eberhardt & David Danks (2011). Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. [REVIEW] Minds and Machines 21 (3):389-410.score: 18.0
    Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the (...)
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  40. Jonah N. Schupbach (2008). On the Alleged Impossibility of Bayesian Coherentism. Philosophical Studies 141 (3):323-331.score: 18.0
    The success of Bovens and Hartmann’s recent “impossibility result” against Bayesian Coherentism relies upon the adoption of a specific set of ceteris paribus conditions. In this paper, I argue that these conditions are not clearly appropriate; certain proposed coherence measures motivate different such conditions and also call for the rejection of at least one of Bovens and Hartmann’s conditions. I show that there exist sets of intuitively plausible ceteris paribus conditions that allow one to sidestep the impossibility result. This (...)
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  41. Desh Raj Sirswal (2012). Methods of Philosophical Inquiry in Upanishads. International Journal of Multidisciplinary Educational Research 1 (2):57-62.score: 18.0
    Philosophy is a subject which does not concerned only to an expert or specialist. It appears that there is probably no human being who does not philosophise. Good philosophy expands one’s imagination as some philosophy is close to us, whoever we are. Then of course some is further away, and some is further still, and some is very alien indeed. We raise questions about the assumptions, presuppositions, or definitions upon which a field of inquiry is based, and these questions can (...)
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  42. Franz Dietrich & Christian List (2013). Reasons for (Prior) Belief in Bayesian Epistemology. Synthese 190 (5):781-786.score: 18.0
    Bayesian epistemology tells us with great precision how we should move from prior to posterior beliefs in light of new evidence or information, but says little about where our prior beliefs come from. It offers few resources to describe some prior beliefs as rational or well-justified, and others as irrational or unreasonable. A different strand of epistemology takes the central epistemological question to be not how to change one’s beliefs in light of new evidence, but what reasons justify a (...)
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  43. Donald Bamber (2000). Entailment with Near Surety of Scaled Assertions of High Conditional Probability. Journal of Philosophical Logic 29 (1):1-74.score: 18.0
    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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  44. Adam J. L. Harris & Magda Osman (2012). The Illusion of Control: A Bayesian Perspective. Synthese 189 (S1):29-38.score: 18.0
    In the absence of an objective contingency, psychological studies have shown that people nevertheless attribute outcomes to their own actions. Thus, by wrongly inferring control in chance situations people appear to hold false beliefs concerning their agency, and are said to succumb to an illusion of control (IoC). In the current article, we challenge traditional conceptualizations of the illusion by examining the thesis that the IoC reflects rational and adaptive decision making. Firstly, we propose that the IoC is a by-product (...)
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  45. T. Barakat & H. A. Alhendi (2013). Generalized Dirac Equation with Induced Energy-Dependent Potential Via Simple Similarity Transformation and Asymptotic Iteration Methods. Foundations of Physics 43 (10):1171-1181.score: 18.0
    This study shows how precise simple analytical solutions for the generalized Dirac equation with repulsive vector and attractive energy-dependent Lorentz scalar potentials, position-dependent mass potential, and a tensor interaction term can be obtained within the framework of both similarity transformation and the asymptotic iteration methods. These methods yield a significant improvement over existing approaches and provide more plausible and applicable ways in explaining the pseudospin symmetry’s breaking mechanism in nuclei.
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  46. Michael Baumgartner & Isabelle Drouet (2013). Identifying Intervention Variables. European Journal for Philosophy of Science 3 (2):183-205.score: 18.0
    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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  47. Elaine Doyle, Jane Frecknall-Hughes & Barbara Summers (2009). Research Methods in Taxation Ethics: Developing the Defining Issues Test (Dit) for a Tax-Specific Scenario. [REVIEW] Journal of Business Ethics 88 (1):35 - 52.score: 18.0
    This paper reports on the development of a research instrument designed to explore ethical reasoning in a tax context. This research instrument is a version of the Defining Issues Test (DIT) originally developed by Rest [1979a, Development in Judging Moral Issues (Univer sity of Minnesota Press, Minneapolis, MN); 1979b, Defining Issues Test (University of Minnesota Press, Minneapolis, MN)], but adapted to focus specifically on the environment encountered by tax practitioners. The paper explores reasons for developing a context-(and profession-) specific test, (...)
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  48. Ulysses Paulino Albuquerque (2011). Local Perception of Environmental Change in a Semi-Arid Area of Northeast Brazil: A New Approach for the Use of Participatory Methods at the Level of Family Units. [REVIEW] Journal of Agricultural and Environmental Ethics 24 (5):511-531.score: 18.0
    The diversity of plant resources in the Brazilian semi-arid region is being compromised by practices related to agriculture, pastures, and forest harvesting, especially in areas containing Caatinga vegetation (xeric shrublands and thorn forests). The impact of these practices constitutes a series of complex factors involving local issues, creating a need for further scientific studies on the social-environmental dynamics of natural resource use. Through participatory methods, the present study analyzed people’s representations about local environmental change processes in the Brazilian semi-arid (...)
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  49. Patricia Hansen-Ketchum & Florence Myrick (2008). Photo Methods for Qualitative Research in Nursing: An Ontological and Epistemological Perspective. Nursing Philosophy 9 (3):205-213.score: 18.0
    Abstract The use of photo research methods is influenced by underlying ontological and epistemological assumptions. Variant assumptions about reality and knowledge converge to conceive a relationship between the knower and what can be known. These assumptions provide the rationale for decided ways of engaging participants in the process of scientific inquiry. In this paper, we examine how perspectives of realism and relativism may shape epistemological understandings and influence type and use of photo methods in qualitative research. Based on (...)
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  50. Jon Williamson (2006). Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information 15 (1-2):155-178.score: 18.0
    We present a new framework for combining logic with probability, and demonstrate the application of this framework to breast cancer prognosis. Background knowledge concerning breast cancer prognosis is represented using logical arguments. This background knowledge and a database are used to build a Bayesian net that captures the probabilistic relationships amongst the variables. Causal hypotheses gleaned from the Bayesian net in turn generate new arguments. The Bayesian net can be queried to help decide when one argument attacks (...)
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