Results for 'Statistical inference'

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  1.  25
    Integrating Physical Constraints in Statistical Inference by 11-Month-Old Infants.Stephanie Denison & Fei Xu - 2010 - Cognitive Science 34 (5):885-908.
    Much research on cognitive development focuses either on early-emerging domain-specific knowledge or domain-general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11- to 12.5-month-old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a (...) inference mechanism. Results from three experiments suggest that, first, infants were able to use domain-specific knowledge to override statistical information, reasoning that sometimes a physical constraint is more informative than probabilistic information. Second, we provide the first evidence that infants are capable of applying domain-specific knowledge in probabilistic reasoning by using a physical constraint to exclude one set of objects while computing probabilities over the remaining sets. (shrink)
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  2. Logic of Statistical Inference.Ian Hacking - 2016 - Cambridge University Press.
    One of Ian Hacking's earliest publications, this book showcases his early ideas on the central concepts and questions surrounding statistical reasoning. He explores the basic principles of statistical reasoning and tests them, both at a philosophical level and in terms of their practical consequences for statisticians. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Jan-Willem Romeijn, illuminating its enduring importance and relevance to philosophical enquiry, Hacking's influential and original work has been (...)
     
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  3.  27
    The Logical Foundations of Statistical Inference.Henry Kyburg - 1974 - Reidel.
    At least one of these conceptions of probability underlies any theory of statistical inference (or, to use Neyman's phrase, 'inductive behavior'). ...
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  4.  33
    Error Statistical Modeling and Inference: Where Methodology Meets Ontology.Aris Spanos & Deborah G. Mayo - 2015 - Synthese 192 (11):3533-3555.
    In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key (...)
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  5.  19
    Seeing the Wood for the Trees: Philosophical Aspects of Classical, Bayesian and Likelihood Approaches in Statistical Inference and Some Implications for Phylogenetic Analysis.Daniel Barker - 2015 - Biology and Philosophy 30 (4):505-525.
    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, extra-mathematical grounds relating to the use of a prior or practical grounds. This essay aims to increase understanding of these limits among those with an interest in phylogeny.
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  6. Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference.Zoltan Dienes - 2008 - Palgrave-Macmillan.
    An accessible and illuminating exploration of the conceptual basisof scientific and statistical inference and the practical impact this has on conducting psychological research. The book encourages a critical discussion of the different approaches and looks at some of the most important thinkers and their influence.
     
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  7.  6
    Natural Selection, Adaptive Topographies and the Problem of Statistical Inference: The Moraba scurra Controversy Under the Microscope.Jean-Baptiste Grodwohl - 2017 - Journal of the History of Biology 50 (4):753-796.
    This paper gives a detailed narrative of a controversial empirical research in postwar population genetics, the analysis of the cytological polymorphisms of an Australian grasshopper, Moraba scurra. This research intertwined key technical developments in three research areas during the 1950s and 1960s: it involved Dobzhansky’s empirical research program on cytological polymorphisms, the mathematical theory of natural selection in two-locus systems, and the building of reliable estimates of natural selection in the wild. In the mid-1950s the cytologist Michael White discovered an (...)
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  8.  7
    The Emergence of Probability. Philosophical Study of Early Ideas about Probability, Induction, and Statistical Inference.Ian Hacking - 1977 - Tijdschrift Voor Filosofie 39 (2):353-354.
    Historical records show that there was no real concept of probability in Europe before the mid-seventeenth century, although the use of dice and other randomizing objects was commonplace. Ian Hacking presents a philosophical critique of early ideas about probability, induction, and statistical inference and the growth of this new family of ideas in the fifteenth, sixteenth, and seventeenth centuries. Hacking invokes a wide intellectual framework involving the growth of science, economics, and the theology of the period. He argues (...)
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  9.  77
    Models and Statistical Inference: The Controversy Between Fisher and Neyman–Pearson.Johannes Lenhard - 2006 - British Journal for the Philosophy of Science 57 (1):69-91.
    The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher's account more stringent mathematically. (...)
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  10.  18
    Rational Statistical Inference: A Critical Component for Word Learning.Fei Xu & Joshua B. Tenenbaum - 2001 - Behavioral and Brain Sciences 24 (6):1123-1124.
    In order to account for how children can generalize words beyond a very limited set of labeled examples, Bloom's proposal of word learning requires two extensions: a better understanding of the “general learning and memory abilities” involved, and a principled framework for integrating multiple conflicting constraints on word meaning. We propose a framework based on Bayesian statistical inference that meets both of those needs.
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  11.  29
    Statistical Inference and Quantum Mechanical Measurement.Rodney W. Benoist, Jean-Paul Marchand & Wolfgang Yourgrau - 1977 - Foundations of Physics 7 (11-12):827-833.
    We analyze the quantum mechanical measuring process from the standpoint of information theory. Statistical inference is used in order to define the most likely state of the measured system that is compatible with the readings of the measuring instrument and the a priori information about the correlations between the system and the instrument. This approach has the advantage that no reference to the time evolution of the combined system need be made. It must, however, be emphasized that the (...)
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  12.  20
    When Can Non‐Commutative Statistical Inference Be Bayesian?Miklós Rédei - 1992 - International Studies in the Philosophy of Science 6 (2):129-132.
    Abstract Based on recalling two characteristic features of Bayesian statistical inference in commutative probability theory, a stability property of the inference is pointed out, and it is argued that that stability of the Bayesian statistical inference is an essential property which must be preserved under generalization of Bayesian inference to the non?commutative case. Mathematical no?go theorems are recalled then which show that, in general, the stability can not be preserved in non?commutative context. Two possible (...)
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  13. The Emergence of Probability: A Philosophical Study of Early Ideas About Probability, Induction and Statistical Inference.Ian Hacking - 2006 - Cambridge University Press.
    Historical records show that there was no real concept of probability in Europe before the mid-seventeenth century, although the use of dice and other randomizing objects was commonplace. Ian Hacking presents a philosophical critique of early ideas about probability, induction, and statistical inference and the growth of this new family of ideas in the fifteenth, sixteenth, and seventeenth centuries. Hacking invokes a wide intellectual framework involving the growth of science, economics, and the theology of the period. He argues (...)
     
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  14.  26
    Persistent Experimenters, Stopping Rules, and Statistical Inference.Katie Steele - 2013 - Erkenntnis 78 (4):937-961.
    This paper considers a key point of contention between classical and Bayesian statistics that is brought to the fore when examining so-called ‘persistent experimenters’—the issue of stopping rules, or more accurately, outcome spaces, and their influence on statistical analysis. First, a working definition of classical and Bayesian statistical tests is given, which makes clear that (1) once an experimental outcome is recorded, other possible outcomes matter only for classical inference, and (2) full outcome spaces are nevertheless relevant (...)
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  15.  24
    Can Error-Statistical Inference Function Securely?Kent Staley - unknown
    This paper analyzes Deborah Mayo's error-statistical (ES) account of scientific evidence in order to clarify the kinds of "material postulates" it requires and to explain how those assumptions function. A secondary aim is to explain and illustrate the importance of the security of an inference. After finding that, on the most straightforward reading of the ES account, it does not succeed in its stated aims, two remedies are considered: either relativize evidence claims or introduce stronger assumptions. The choice (...)
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  16.  24
    Statistical Inference and Sensitivity to Sampling in 11-Month-Old Infants.Fei Xu & Stephanie Denison - 2009 - Cognition 112 (1):97-104.
  17. On the Foundations of Statistical Inference.Allan Birnbaum - 1962 - Journal of the American Statistical Association 57 (298):269--306.
     
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  18.  10
    General Properties of Bayesian Learning as Statistical Inference Determined by Conditional Expectations.Zalán Gyenis & Miklós Rédei - unknown
    We investigate the general properties of general Bayesian learning, where “general Bayesian learning” means inferring a state from another that is regarded as evidence, and where the inference is conditionalizing the evidence using the conditional expectation determined by a reference probability measure representing the background subjective degrees of belief of a Bayesian Agent performing the inference. States are linear functionals that encode probability measures by assigning expectation values to random variables via integrating them with respect to the probability (...)
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  19.  33
    On Objectivity and Subjectivity in Statistical Inference: A Response to Mayo.Peffrey A. Witmer & Murray K. Clayton - 1986 - Synthese 67 (2):369 - 379.
    In this paper we respond to the article An Objective Theory of Statistical Testing by D. G. Mayo (1983). We argue that the theory of testing developed by Mayo, NPT*, is neither novel nor objective. We also respond to the claims made by Mayo against Bayesian theory.
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  20.  11
    Comment on Chow's "Issues in Statistical Inference".Christopher D. Green - 2002 - Philosophical Explorations.
    Contrary to Chow, Wilkinson's report, though more tentative than it might have been, is a reasoned and valuable contribution to psychological science. For those who are quite familiar with the details of statistical methods, it confirms much of what has been happening in the literature over the past few decades. For those who have not been keeping abreast of new developments on the statistical scene, it alerts them in a gentle way that there have been some important changes (...)
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  21. The Influence of Categories on Perception: Explaining the Perceptual Magnet Effect as Optimal Statistical Inference.Naomi H. Feldman, Thomas L. Griffiths & James L. Morgan - 2009 - Psychological Review 116 (4):752-782.
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  22.  1
    Bayesian Statistical Inference in Psychology: Comment on Trafimow.Michael D. Lee & Eric-Jan Wagenmakers - 2005 - Psychological Review 112 (3):662-668.
  23.  1
    The Logic of Statistical Inference.I. Hacking - 1972 - British Journal for the Philosophy of Science 23 (2):123-132.
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  24. Individuation, Counting, and Statistical Inference: The Role of Frequency and Whole-Object Representations in Judgment Under Uncertainty.Gary L. Brase, Leda Cosmides & John Tooby - 1998 - Journal of Experimental Psychology: General 127 (1):3-21.
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  25. Measured Realism and Statistical Inference: An Explanation for the Fast Progress of "Hard" Psychology.J. D. Trout - 1999 - Philosophy of Science 66 (3):272.
    The use of null hypothesis significance testing (NHST) in psychology has been under sustained attack, despite its reliable use in the notably successful, so-called "hard" areas of psychology, such as perception and cognition. I argue that, in contrast to merely methodological analyses of hypothesis testing (in terms of "test severity," or other confirmation-theoretic notions), only a patently metaphysical position can adequately capture the uneven but undeniable successes of theories in "hard psychology." I contend that Measured Realism satisfies this description, and (...)
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  26. Rational Statistical Inference and Cognitive Development.Fei Xu - 2007 - In Peter Carruthers (ed.), The Innate Mind: Foundations and the Future. Oxford University Press, Usa. pp. 3--199.
     
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  27.  25
    Statistical Explanation Vs. Statistical Inference.Richard C. Jeffrey - 1969 - In Nicholas Rescher (ed.), Essays in Honor of Carl G. Hempel. Reidel. pp. 104--113.
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  28. Statistical Inference and Analysis Selected Correspondence of R.A. Fisher.Ronald Aylmer Fisher & J. H. Bennett - 1990
     
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  29.  24
    Rational Constructivism, Statistical Inference, and Core Cognition.Fei Xu & Susan Carey - 2011 - Behavioral and Brain Sciences 34 (3):151.
    I make two points in this commentary on Carey (2009). First, it may be too soon to conclude that core cognition is innate. Recent advances in computational cognitive science and developmental psychology suggest possible mechanisms for developing inductive biases. Second, there is another possible answer to Fodor's challenge – if concepts are merely mental tokens, then cognitive scientists should spend their time on developing a theory of belief fixation instead.
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  30.  21
    Theory Change and Bayesian Statistical Inference.Jan-Willem Romeijn - 2005 - Philosophy of Science 72 (5):1174-1186.
  31.  5
    Philosophical Problems of Statistical Inference.Teddy Seidenfeld - 1981 - Philosophical Review 90 (2):295-298.
  32. Philosophical Problems of Statistical Inference Learning From R. A. Fisher.T. SEIDENFELD - 1979 - D. Reidel Publishing Co..
     
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  33.  1
    Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science.W. L. Harper & C. A. Hooker - 1978 - Studia Logica 37 (2):213-219.
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  34. Two Points in the Theory of Statistical Inference.G. A. Barnard - 1972 - British Journal for the Philosophy of Science 23 (4):329-331.
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  35.  87
    The Logic of Statistical Inference.G. A. Barnard - 1972 - British Journal for the Philosophy of Science 23 (2):123-132.
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  36.  44
    Statistical Inference: Learning in Artificial Neural Networks.Howard Hua Yang, Noboru Murata & Shun-Ichi Amari - 1998 - Trends in Cognitive Sciences 2 (1):4-10.
  37.  1
    General Properties of Bayesian Learning as Statistical Inference Determined by Conditional Expectations.Zalán Gyenis & Miklós Rédei - 2017 - Review of Symbolic Logic 10 (4):719-755.
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  38.  32
    Addendum to Statistical Inference and Quantum Mechanical Measurement.Rodney W. Benoist, Jean-Paul Marchand & Wolfgang Yourgrau - 1978 - Foundations of Physics 8 (1-2):117-118.
  39.  24
    Statistical Inference Without Frequentist Justifications.Jan Sprenger - 2010 - In M. Dorato M. Suàrez (ed.), Epsa Epistemology and Methodology of Science. Springer. pp. 289--297.
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  40.  17
    Statistical Inference as a Model for Learning in ANNs.Howard Hua Yangy, Noboru Murataz & Shun-Ichi Amariz - 1998 - Trends in Cognitive Sciences 2 (1):4-10.
  41.  22
    The Emergence of Probability: A Philosophical Study of Early Ideas About Probability, Induction and Statistical Inference.John M. Vickers - 1976 - Journal of the History of Philosophy 14 (3):366-367.
  42.  14
    Statistical Inference for Measures of Predictive Success.Thomas Demuynck - 2015 - Theory and Decision 79 (4):689-699.
  43.  15
    On Some Basic Patterns of Statistical Inference.Klemens Szaniawski - 1961 - Studia Logica 11 (1):77 - 89.
  44.  12
    The Logical Foundations of Statistical Inference[REVIEW]Teddy Seidenfeld - 1977 - Journal of Philosophy 74 (1):47-62.
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  45.  5
    Logic of Statistical Inference. By Ian Hacking. Cambridge University Press; Toronto: Macmillan of Canada, 1965. Pp. Ix, 227. $6.75. [REVIEW]Alex C. Michalos - 1967 - Dialogue 5 (4):647-649.
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  46.  11
    Review: Gerhard Tintner, Foundations of Probability and Statistical Inference; G. A. Barnard, F. J. Anscombe, Bartlett, W. Perks, J. R. N. Stone, J. O. Irwin, G. Tintner, Discussion. [REVIEW]Richard C. Jeffrey - 1972 - Journal of Symbolic Logic 37 (3):630-631.
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  47.  12
    Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science.W. Hooker, C., Harper (ed.) - 1976 - Springer.
  48.  5
    Tintner Gerhard. Foundations of Probability and Statistical Inference. The Journal of the Royal Statistical Society, Ser. A Vol. 112 Part 3 , Pp. 251–279.Barnard G. A., Anscombe F. J., Bartlett, Perks W., Stone J. R. N., Irwin J. O., and Tintner G.. Discussion. The Journal of the Royal Statistical Society, Ser. A Vol. 112 Part 3 , Pp. 280–286. [REVIEW]Richard C. Jeffrey - 1972 - Journal of Symbolic Logic 37 (3):630-631.
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  49. The Role of Inductive Logic in Statistical Inference.Domenico Costantini - 1984 - Epistemologia 7:153.
  50.  19
    Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science.Bernd I. Dahn - 1978 - Studia Logica 37 (2):213-219.
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