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Clark Glymour [193]Clark N. Glymour [1]
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Profile: Clark Glymour (Carnegie Mellon University)
  1. David Danks & Clark Glymour, Linearity Properties of Bayes Nets with Binary Variables.
    It is “well known” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables” sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a (...)
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  2. Phil Dowe, Paul Noordhof & Clark Glymour, Florida Institute for Human and Machine Cognition.
    For most of the contributions to this volume, the project is this: Fill out “Event X is a cause of event Y if and only if……” where the dots on the right are to be filled in by a claims formulated in terms using any of (1) descriptions of possible worlds and their relations; (2) a special predicate, “is a law;” (3) “chances;” and (4) anything else one thinks one needs. The form of analysis is roughly the same as that (...)
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  3. Clark Glymour, Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges.
    We applied TETRAD II, a causal discovery program developed in Carnegie Mellon University’s Department of Philosophy, to a database containing information on 204 U.S. colleges, collected by the US News and World Report magazine for the purpose of college ranking. Our analysis focuses on possible causes of low freshmen retention in U.S. colleges. TETRAD II finds a set of causal structures that are compatible with the data.
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  4. Clark Glymour, Bootstraps and Probabilities L Nu ®.
    The Joumal 0f Philosophy, Vol. 77, No. 11, Seventy—Seventh Annual Meeting American Philosophical Association, Eastern Division (Nov., 1980), 691-699.
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  5. Clark Glymour, Correction L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  6. Clark Glymour, Determinism, Ignorance, and Quanttun Mechanics L Nu ®.
    The Joumal 0f Philosophy, Vol. 68, No. 21, Sixty-Eighth Annual Meeting of the American Philosophical Association Eastern Division (Nov. 4, 1971), 744-751.
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  7. Clark Glymour, Having the Right Tool: Causal Graphs in Teaching Research Design.
    A general principle for good pedagogic strategy is this: other things equal, make the essential principles of the subject explicit rather than tacit. We think that this principle is routinely violated in conventional instruction in statistics. Even though most of the early history of probability theory has been driven by causal considerations, the terms “cause” and “causation” have practically disappeared from statistics textbooks. Statistics curricula guide students away from the concept of causality, into remembering perhaps the cliche disclaimer “correlation does (...)
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  8. Clark Glymour, On Some Patterns of Reduction L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  9. Clark Glymour, Pages 1- 10.
    sities. TETRAD II discovers a class of possible causal structures of a system from a data set containing measurements of the system variables. The signi cance of learning the causal structure of a system is that it allows for predicting the e ect of interventions into the system, crucial in policy making. Our data sets contained information on 204 U.S. national universities, collected by the US News and World Report magazine for the purpose of college ranking in 1992 and 1993. (...)
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  10. Clark Glymour, Physics by Convention L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  11. Clark Glymour, Relevant Evidence L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  12. Clark Glymour, Revisions of Bootstrap Testing L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  13. Clark Glymour, Statistics and Causal Inference: Comment: Statistics and Metaphysics L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  14. Clark Glymour, The Epistemology of Geometry L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  15. Clark Glymour, The Stun Rule is Well-Confirmed L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  16. Clark Glymour, To Save the Noumena L Nu ®.
    Your use of the JSTOR archive indicates your acceptance of J STOR’s Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. J STOR’s Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non—commercial use.
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  17. Clark Glymour & Richard Scheines, On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables.
    We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log2(N ) + 1 experiments are sufficient and in the worst case necessary to determine the causal relations among N ≥ 2 variables when no latent variables, no sample selection bias and no feedback cycles are present. For all K, 0 < K <.
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  18. Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz, Causal Learning in Children: Causal Maps and Bayes Nets.
    We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig causal Bayes nets and for predicting with (...)
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  19. Joseph Ramsey & Clark Glymour, Experiments on the Accuracy of Algorithms for Inferring the Structure of Genetic Regulatory Networks From Microarray Expression Levels.
    After reviewing theoretical reasons for doubting that machine learning methods can accurately infer gene regulatory networks from microarray data, we test 10 algorithms on simulated data from the sea urchin network, and on microarray data for yeast compared with recent experimental determinations of the regulatory network in the same yeast species. Our results agree with the theoretical arguments: most algorithms are at chance for determining the existence of a regulatory connection between gene pairs, and the algorithms that perform better than (...)
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  20. Joseph Ramsey, Peter Spirtes & Clark Glymour, Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition.
    Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the (...)
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  21. Richard Scheines, Peter Spirtes & Clark Glymour, Building Latent Variable Models'.
    Researchers routinely face the problem of inferring causal relationships from large amounts of data, sometimes involving hundreds of variables. Often, it is the causal relationships between "latent" (unmeasured) variables that are of primary interest. The problem is how causal relationships between unmeasured variables can be inferred from measured data. For example, naval manpower researchers have been asked to infer the causal relations among psychological traits such as job satisfaction and job challenge from a data base in which neither trait is (...)
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  22. Peter Spirtes & Clark Glymour, Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition.
    Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the (...)
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  23. Peter Spirtes & Clark Glymour, With Unmeasured Variables.
    In recent papers we have described a framework for inferring causal structure from relations of statistical independence among a set of measured variables. Using Pearl's notion of the perfect representation of a set of independence relations by a directed acyclic graph we proved..
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  24. Peter Spirtes, Clark Glymour & Richard Scheines, Causality From Probability.
    Data analysis that merely fits an empirical covariance matrix or that finds the best least squares linear estimator of a variable is not of itself a reliable guide to judgements about policy, which inevitably involve causal conclusions. The policy implications of empirical data can be completely reversed by alternative hypotheses about the causal relations of variables, and the estimates of a particular causal influence can be radically altered by changes in the assumptions made about other dependencies.2 For these reasons, one (...)
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  25. Peter Spirtes, Thomas Richardson, Christopher Meek, Richard Scheines & Clark Glymour, Using D-Separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors.
    It has been shown in Spirtes(1995) that X and Y are d-separated given Z in a directed graph associated with a recursive or non-recursive linear model without correlated errors if and only if the model entails that ρXY.Z = 0. This result cannot be directly applied to a linear model with correlated errors, however, because the standard graphical representation of a linear model with correlated errors is not a directed graph. The main result of this paper is to show how (...)
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  26. Clark Glymour, Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition.
    Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the (...)
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  27. Clark Glymour, Classification and Filtering of Spectra: A Case Study in Mineralogy.
    The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. Even though expert knowledge is commonly used for this task, it is desirable to create automated systems with similar or better performance. For instance, NASA intends to design robots that are sufficiently autonomous to perform this task on planetary missions. Spectrometer readings provide one important source of data for identifying sites with minerals of interest. Reflectance spectrometers measure intensities of (...)
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  28. Clark Glymour, Causal Mechanism and Probability: A Normative Approach.
    & Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic (...)
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  29. Clark Glymour, Data Driven Methods for Granger Causality and Contemporaneous Causality with Non-Linear Corrections: Climate Teleconnection Mechanisms.
    We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the indices, (...)
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  30. Clark Glymour, Editorial.
    The use of ceteris paribus clauses in philosophy and in the sciences has a long and fascinating history. Persky (1990) traces the use by economists of ceteris paribus clauses in qualifying generalizations as far back as William Petty’s Treatise of Taxes and Contributions (1662). John Cairnes’ The Character and Logical Method of Political Economy (1857) is credited with enunciating the idea that the conclusions of economic investigations hold “only in the absence of disturbing causes”.1 His Leading Principles (1874) contains the (...)
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  31. Clark Glymour, Evidence of Systematic Expressed Sequence Tag Image Clone Cross-Hybridization on Cdna Microarrays.
    We present evidence of a potentially serious source of error intrinsic to all spotted cDNA microarrays that use IMAGE clones of expressed sequence tags (ESTs). We found that a high proportion of these EST sequences contain 5V-end poly(dT) sequences that are remnants from the oligo(dT)-primed reverse transcription of polyadenylated mRNA templates used to generate EST cDNA for sequence clone libraries. Analysis of expression data from two single-dye cDNA microarray experiments showed that ESTs whose sequences contain repeats of consecutive 5V- end (...)
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  32. Clark Glymour, Essay Review: Cause and Chance: Causation in an Indeterministic World, Phil Dowe and Paul Noordhof, Eds., Routledge, 2004.
    For most of the contributions to this volume, the project is this: Fill out “Event X is a cause of event Y if and only if……” where the dots on the right are to be filled in by a claims formulated in terms using any of (1) descriptions of possible worlds and their relations; (2) a special predicate, “is a law;” (3) “chances;” and (4) anything else one thinks one needs. The form of analysis is roughly the same as that (...)
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  33. Clark Glymour, Linearity Properties of Bayes Nets with Binary Variables.
    It is “well known” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables” sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a (...)
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  34. Clark Glymour, 5. Markov Properties and Quantum Experiments.
    Few people have thought so hard about the nature of the quantum theory as has Jeff Bub,· and so it seems appropriate to offer in his honor some reflections on that theory. My topic is an old one, the consistency of our microscopic theories with our macroscopic theories, my example, the Aspect experiments (Aspect et al., 1981, 1982, 1982a; Clauser and Shimony, l978;_Duncan and Kleinpoppen, 199,8) is familiar, and my sirnplrcation of it is borrowed. All that is new here is (...)
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  35. Clark Glymour, Running Head: Conditional Interventions.
    The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of the causal Bayes net formalism. Four experiments suggest that preschoolers can use the conditional intervention principle both to learn complex causal structure from patterns of evidence and to predict patterns of evidence from knowledge of causal structure. Other theories of causal learning do not account for these results.
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  36. Clark Glymour, Statistical Themes and Lessons for Data Mining.
    Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further progress in data analysis.
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  37. Clark Glymour, The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search.
    Our primary interest is in determining how many gene perturbation experiments are required to determine the Various algorithms have been proposed for learning..
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  38. Clark Glymour, Two Statistical Problems for Inference to Regulatory Structure From Associations of Gene Expression Measurements with Microarrays.
    Of the many proposals for inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization, several aim to estimate regulatory structure from the associations of gene expression levels measured in repeated samples. The repeated samples may be from a single experimental condition, or from several distinct experimental conditions; they may be “equilibrium” measurements or time series; the associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. This paper describes two (...)
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  39. Clark Glymour, Using Path Diagrams as a Structural Equation Modelling Tool.
    Linear structural equation models (SEMs) are widely used in sociology, econometrics, biology, and other sciences. A SEM (without free parameters) has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the “error” or “disturbance” terms), and an associated path diagram corresponding to the causal relations among variables specified by the structural equations and the correlations among the error terms. It is often thought that the path diagram is (...)
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  40. Clark Glymour, Words, Thoughts and Theories.
    Words, Thoughts and Theories argues that infants and children discover the physical and psychological features of the world by a process akin to scientific inquiry, more or less as conceived by philosophers of science in the 1960s (the theory theory). This essay discusses some of the philosophical background to an alternative, more popular, “modular” or “maturational” account of development, dismisses an array of philosophical objections to the theory theory, suggests that the theory theory offers an undeveloped project for artificial intelligence, (...)
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  41. Richard Scheines, Clark Glymour & Peter Spirtes, Learning the Structure of Linear Latent Variable Models.
    We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded (...)
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  42. Peter Spirtes, Clark Glymour & Richard Scheines, Automated Search for Causal Relations - Theory and Practice.
    nature of modern data collection and storage techniques, and the increases in the speed and storage capacities of computers. Statistics books from 30 years ago often presented examples with fewer than 10 variables, in domains where some background knowledge was plausible. In contrast, in new domains, such as climate research where satellite data now provide daily quantities of data unthinkable a few decades ago, fMRI brain imaging, and microarray measurements of gene expression, the number of variables can range into the (...)
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  43. Clark Glymour (2014). Poincaré's Probabilities, Kantified, Post-Modernized. Biological Theory 9 (1):113-114.
  44. Clark Glymour (2013). Theoretical Equivalence and the Semantic View of Theories. Philosophy of Science 80 (2):286-297.
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  45. Marc Lange, Peter Vickers, John Michael, Miles MacLeod, Alexander R. Pruss, David John Baker, Clark Glymour & Simon Fitzpatrick (2013). 1. Really Statistical Explanations and Genetic Drift Really Statistical Explanations and Genetic Drift (Pp. 169-188). Philosophy of Science 80 (2).
     
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  46. Clark Glymour (2012). On the Possibility of Inference to the Best Explanation. Journal of Philosophical Logic 41 (2):461-469.
    Various proposals have suggested that an adequate explanatory theory should reduce the number or the cardinality of the set of logically independent claims that need be accepted in order to entail a body of data. A (and perhaps the only) well-formed proposal of this kind is William Kneale’s: an explanatory theory should be finitely axiomatizable but it’s set of logical consequences in the data language should not be finitely axiomatizable. Craig and Vaught showed that Kneale theories (almost) always exist for (...)
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  47. Arthur Paul Pedersen & Clark Glymour (2012). What Language Dependence Problem? A Reply for Joyce to Fitelson on Joyce. Philosophy of Science 79 (4):561-574.
    In an essay recently published in this journal, Branden Fitelson argues that a variant of Miller’s argument for the language dependence of the accuracy of predictions can be applied to Joyce’s notion of accuracy of credences formulated in terms of scoring rules, resulting in a general potential problem for Joyce’s argument for probabilism. We argue that no relevant problem of the sort Fitelson supposes arises since his main theorem and his supporting arguments presuppose the validity of nonlinear transformations of credence (...)
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  48. Clark Glymour (2011). Osiander's psychology. Behavioral and Brain Sciences 34 (4):199-200.
    Bayesian psychology follows an old instrumentalist tradition most infamously illustrated by Osiander's preface to Copernicus's masterpiece. Jones & Love's (J&L's) criticisms are, if anything, understated, and their proposals overoptimistic.
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  49. David Danks, Stephen Fancsali, Clark Glymour & Richard Scheines (2010). Comorbid Science? Behavioral and Brain Sciences 33 (2-3):153 - 155.
    We agree with Cramer et al.'s goal of the discovery of causal relationships, but we argue that the authors' characterization of latent variable models (as deployed for such purposes) overlooks a wealth of extant possibilities. We provide a preliminary analysis of their data, using existing algorithms for causal inference and for the specification of latent variable models.
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  50. Michael Freenor & Clark Glymour, Searching the DCM Model Space, and Some Generalizations.
    We describe the (enormous) size of the search space for Dynamic Casual Models and generalizations of them.
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