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  1. added 2020-05-20
    Trung tâm ISR có bài ra mừng 130 năm Ngày sinh Chủ tịch Hồ Chí Minh.Hồ Mạnh Toàn - 2020 - ISR Phenikaa 2020 (5):1-3.
    Bài mới xuất bản vào ngày 19-5-2020 với tác giả liên lạc là NCS Nguyễn Minh Hoàng, cán bộ nghiên cứu của Trung tâm ISR, trình bày tiếp cận thống kê Bayesian cho việc nghiên cứu dữ liệu khoa học xã hội. Đây là kết quả của định hướng Nhóm nghiên cứu SDAG được nêu rõ ngay từ ngày 18-5-2019.
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  2. added 2020-05-05
    Improving Bayesian Statistics Understanding in the Age of Big Data with the Bayesvl R Package.Quan-Hoang Vuong, Viet-Phuong La, Minh-Hoang Nguyen, Manh-Toan Ho, Manh-Tung Ho & Peter Mantello - 2020 - Software Impacts 4 (1):100016.
    The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the ability (...)
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  3. added 2020-03-10
    Clinical Equipoise and Adaptive Clinical Trials.Nicolas Fillion - 2019 - Topoi 38 (2):457-467.
    Ethically permissible clinical trials must not expose subjects to risks that are unreasonable in relation to anticipated benefits. In the research ethics literature, this moral requirement is typically understood in one of two different ways: as requiring the existence of a state of clinical equipoise, meaning a state of honest, professional disagreement among the community of experts about the preferred treatment; or as requiring an equilibrium between individual and collective ethics. It has been maintained that this second interpretation makes it (...)
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  4. added 2020-02-28
    Tiến triển của bayesvl kể từ sau khi xuất bản trên CRAN.Vương Quân Hoàng & Lã Việt Phương - manuscript
  5. added 2020-02-02
    Inflated Effect Sizes and Underpowered Tests: How the Severity Measure of Evidence is Affected by the Winner's Curse.Guillaume Rochefort-Maranda - forthcoming - Philosophical Studies.
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  6. added 2019-12-24
    Nhà khoa học Việt đứng tên một mình trên tạp chí hàng đầu về khoa học dữ liệu của Nature Research.Thùy Dương - 2017 - Dân Trí Online 2017 (10).
    Dân Trí (25/10/2017) — Lần đầu tiên, có một nhà khoa học người Việt, thực hiện công trình nghiên cứu hoàn toàn 100% tại Việt Nam, đứng tên một mình được công bố tại tạp chí Scientific Data, một tạp chí hàng đầu về khoa học dữ liệu thuộc danh mục xuất bản của Nature Research danh tiếng.
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  7. added 2019-11-25
    Two Impossibility Results for Measures of Corroboration.Jan Sprenger - 2018 - British Journal for the Philosophy of Science 69:139--159.
    According to influential accounts of scientific method, such as critical rationalism, scientific knowledge grows by repeatedly testing our best hypotheses. But despite the popularity of hypothesis tests in statistical inference and science in general, their philosophical foundations remain shaky. In particular, the interpretation of non-significant results—those that do not reject the tested hypothesis—poses a major philosophical challenge. To what extent do they corroborate the tested hypothesis, or provide a reason to accept it? Popper sought for measures of corroboration that could (...)
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  8. added 2019-11-25
    Statistical Inference Without Frequentist Justifications.Jan Sprenger - 2010 - In M. Dorato M. Suàrez (ed.), Epsa Epistemology and Methodology of Science. Springer. pp. 289--297.
    Statistical inference is often justified by long-run properties of the sampling distributions, such as the repeated sampling rationale. These are frequentist justifications of statistical inference. I argue, in line with existing philosophical literature, but against a widespread image in empirical science, that these justifications are flawed. Then I propose a novel interpretation of probability in statistics, the artefactual interpretation. I believe that this interpretation is able to bridge the gap between statistical probability calculations and rational decisions on the basis of (...)
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  9. added 2019-11-25
    Evidence and Experimental Design in Sequential Trials.Jan Sprenger - 2009 - Philosophy of Science 76 (5):637-649.
    To what extent does the design of statistical experiments, in particular sequential trials, affect their interpretation? Should postexperimental decisions depend on the observed data alone, or should they account for the used stopping rule? Bayesians and frequentists are apparently deadlocked in their controversy over these questions. To resolve the deadlock, I suggest a three‐part strategy that combines conceptual, methodological, and decision‐theoretic arguments. This approach maintains the pre‐experimental relevance of experimental design and stopping rules but vindicates their evidential, postexperimental irrelevance. †To (...)
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  10. added 2019-11-25
    Statistics Between Inductive Logic and Empirical Science.Jan Sprenger - 2009 - Journal of Applied Logic 7 (2):239--250.
    Inductive logic generalizes the idea of logical entailment and provides standards for the evaluation of non-conclusive arguments. A main application of inductive logic is the generalization of observational data to theoretical models. In the empirical sciences, the mathematical theory of statistics addresses the same problem. This paper argues that there is no separable purely logical aspect of statistical inference in a variety of complex problems. Instead, statistical practice is often motivated by decision-theoretic considerations and resembles empirical science.
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  11. added 2019-11-08
    An Automatic Ockham’s Razor for Bayesians?Gordon Belot - 2019 - Erkenntnis 84 (6):1361-1367.
    It is sometimes claimed that the Bayesian framework automatically implements Ockham’s razor—that conditionalizing on data consistent with both a simple theory and a complex theory more or less inevitably favours the simpler theory. It is shown here that the automatic razor doesn’t in fact cut it for certain mundane curve-fitting problems.
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  12. added 2019-10-17
    Conditional Degree of Belief and Bayesian Inference.Jan Sprenger - 2020 - Philosophy of Science 87 (2):319-335.
    Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for Bayesian (...)
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  13. added 2019-08-11
    Misalignment Between Research Hypotheses and Statistical Hypotheses: A Threat to Evidence-Based Medicine?Insa Lawler & Georg Zimmermann - forthcoming - Topoi:1-12.
    Evidence-based medicine frequently uses statistical hypothesis testing. In this paradigm, data can only disconfirm a research hypothesis’ competitors: One tests the negation of a statistical hypothesis that is supposed to correspond to the research hypothesis. In practice, these hypotheses are often misaligned. For instance, directional research hypotheses are often paired with non-directional statistical hypotheses. Prima facie, one cannot gain proper evidence for one’s research hypothesis employing a misaligned statistical hypothesis. This paper sheds lights on the nature of and the reasons (...)
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  14. added 2019-07-15
    Why is Bayesian Confirmation Theory Rarely Practiced?Robert W. P. Luk - 2019 - Science and Philosophy 7 (1):3-20.
    Bayesian confirmation theory is a leading theory to decide the confirmation/refutation of a hypothesis based on probability calculus. While it may be much discussed in philosophy of science, is it actually practiced in terms of hypothesis testing by scientists? Since the assignment of some of the probabilities in the theory is open to debate and the risk of making the wrong decision is unknown, many scientists do not use the theory in hypothesis testing. Instead, they use alternative statistical tests that (...)
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  15. added 2019-06-21
    Handbook of the Philosophy of Science, Vol. 7: Philosophy of Statistics.Prasanta S. Bandyopadhyay & Malcolm Forster (eds.) - 2011 - Elsevier B.V..
  16. added 2019-05-03
    Mereological Dominance and Simpson’s Paradox.Tung-Ying Wu - 2020 - Philosophia: Philosophical Quarterly of Israel 48 (1):391–404.
    Numerous papers have investigated the transitivity principle of ‘better-than.’ A recent argument appeals to the principle of mereological dominance for transitivity. However, writers have not treated mereological dominance in much detail. This paper sets out to evaluate the generality of mereological dominance and its effectiveness in supporting the transitivity principle. I found that the mereological dominance principle is vulnerable to a counterexample based on Simpson’s Paradox. The thesis concludes that the mereological dominance principle should be revised in certain ways.
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  17. added 2019-03-07
    Bias and Conditioning in Sequential Medical Trials.Cecilia Nardini & Jan Sprenger - 2013 - Philosophy of Science 80 (5):1053-1064.
    Randomized Controlled Trials are currently the gold standard within evidence-based medicine. Usually, they are conducted as sequential trials allowing for monitoring for early signs of effectiveness or harm. However, evidence from early stopped trials is often charged with being biased towards implausibly large effects. To our mind, this skeptical attitude is unfounded and caused by the failure to perform appropriate conditioning in the statistical analysis of the evidence. We contend that a shift from unconditional hypothesis tests in the style of (...)
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  18. added 2019-01-08
    Expectational V. Instrumental Reasoning: What Statistics Contributes to Practical Reasoning.Mariam Thalos - 2017 - Diametros 53:125-149.
    Utility theories—both Expected Utility and non-Expected Utility theories—offer numericalized representations of classical principles meant for the regulation of choice under conditions of risk—a type of formal representation that reduces the representation of risk to a single number. I shall refer to these as risk-numericalizing theories of decision. I shall argue that risk--numericalizing theories are not satisfactory answers to the question: “How do I take the means to my ends?” In other words, they are inadequate or incomplete as instrumental theories. They (...)
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  19. added 2019-01-05
    Direct Inference in the Material Theory of Induction.William Peden - 2019 - Philosophy of Science 86 (4):672-695.
    John D. Norton’s “Material Theory of Induction” has been one of the most intriguing recent additions to the philosophy of induction. Norton’s account appears to be a notably natural account of actual inductive practices, although his theory has attracted considerable criticism. I detail several novel issues for his theory but argue that supplementing the Material Theory with a theory of direct inference could address these problems. I argue that if this combination is possible, a stronger theory of inductive reasoning emerges, (...)
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  20. added 2019-01-05
    Imprecise Probability and the Measurement of Keynes's "Weight of Arguments".William Peden - 2018 - IfCoLog Journal of Logics and Their Applications 5 (4):677-708.
    Many philosophers argue that Keynes’s concept of the “weight of arguments” is an important aspect of argument appraisal. The weight of an argument is the quantity of relevant evidence cited in the premises. However, this dimension of argumentation does not have a received method for formalisation. Kyburg has suggested a measure of weight that uses the degree of imprecision in his system of “Evidential Probability” to quantify weight. I develop and defend this approach to measuring weight. I illustrate the usefulness (...)
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  21. added 2018-09-24
    Causal Conclusions That Flip Repeatedly and Their Justification.Kevin T. Kelly & Conor Mayo-Wilson - 2010 - Proceedings of the Twenty Sixth Conference on Uncertainty in Artificial Intelligence 26:277-286.
    Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method from non-experimental data is subject to reversal as the sample size increases any finite number of times. That result, called the causal flipping theorem, extends prior results to the effect that causal discovery (...)
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  22. added 2018-07-09
    Multiple Regression Is Not Multiple Regressions: The Meaning of Multiple Regression and the Non-Problem of Collinearity.Michael B. Morrissey & Graeme D. Ruxton - 2018 - Philosophy, Theory, and Practice in Biology 10 (3).
    Simple regression (regression analysis with a single explanatory variable), and multiple regression (regression models with multiple explanatory variables), typically correspond to very different biological questions. The former use regression lines to describe univariate associations. The latter describe the partial, or direct, effects of multiple variables, conditioned on one another. We suspect that the superficial similarity of simple and multiple regression leads to confusion in their interpretation. A clear understanding of these methods is essential, as they underlie a large range of (...)
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  23. added 2018-06-01
    A Merton Model of Credit Risk with Jumps.Hoang Thi Phuong Thao & Quan-Hoang Vuong - 2015 - Journal of Statistics Applications and Probability Letters 2 (2):97-103.
    In this note, we consider a Merton model for default risk, where the firm’s value is driven by a Brownian motion and a compound Poisson process.
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  24. added 2018-05-30
    Tychomancy: Inferring Probability From Causal Structure.Michael Strevens - 2013 - Cambridge, MA: Harvard University Press.
    Maxwell's deduction of the probability distribution over the velocity of gas molecules—one of the most important passages in physics (Truesdell)—presents a riddle: a physical discovery of the first importance was made in a single inferential leap without any apparent recourse to empirical evidence. -/- Tychomancy proposes that Maxwell's derivation was not made a priori; rather, he inferred his distribution from non-probabilistic facts about the dynamics of intermolecular collisions. Further, the inference is of the same sort as everyday reasoning about the (...)
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  25. added 2018-05-22
    Derivation of the Cramer-Rao Bound.Ryan Reece - manuscript
    I give a pedagogical derivation of the Cramer-Rao Bound, which gives a lower bound on the variance of estimators used in statistical point estimation, commonly used to give numerical estimates of the systematic uncertainties in a measurement.
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  26. added 2018-02-16
    Pragmatic Warrant for Frequentist Statistical Practice: The Case of High Energy Physics.Kent Staley - 2017 - Synthese 194 (2).
    Amidst long-running debates within the field, high energy physics has adopted a statistical methodology that primarily employs standard frequentist techniques such as significance testing and confidence interval estimation, but incorporates Bayesian methods for limited purposes. The discovery of the Higgs boson has drawn increased attention to the statistical methods employed within HEP. Here I argue that the warrant for the practice in HEP of relying primarily on frequentist methods can best be understood as pragmatic, in the sense that statistical methods (...)
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  27. added 2017-12-23
    Legal Burdens of Proof and Statistical Evidence.Georgi Gardiner - forthcoming - In James Chase & David Coady (eds.), The Routledge Handbook of Applied Epistemology. Routledge.
    In order to perform certain actions – such as incarcerating a person or revoking parental rights – the state must establish certain facts to a particular standard of proof. These standards – such as preponderance of evidence and beyond reasonable doubt – are often interpreted as likelihoods or epistemic confidences. Many theorists construe them numerically; beyond reasonable doubt, for example, is often construed as 90 to 95% confidence in the guilt of the defendant. -/- A family of influential cases suggests (...)
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  28. added 2017-12-03
    Evidence Amalgamation, Plausibility, and Cancer Research.Marta Bertolaso & Fabio Sterpetti - 2019 - Synthese 196 (8):3279-3317.
    Cancer research is experiencing ‘paradigm instability’, since there are two rival theories of carcinogenesis which confront themselves, namely the somatic mutation theory and the tissue organization field theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We think instead (...)
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  29. added 2017-07-27
    What is a Philosophical Effect? Models of Data in Experimental Philosophy.Bryce Huebner - 2015 - Philosophical Studies 172 (12):3273-3292.
    Papers in experimental philosophy rarely offer an account of what it would take to reveal a philosophically significant effect. In part, this is because experimental philosophers tend to pay insufficient attention to the hierarchy of models that would be required to justify interpretations of their data; as a result, some of their most exciting claims fail as explanations. But this does not impugn experimental philosophy. My aim is to show that experimental philosophy could be made more successful by developing, articulating, (...)
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  30. added 2017-07-18
    David Van Dantzig's Statistical Work.J. Hemelrijk - 1959 - Synthese 11 (4):335 - 351.
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  31. added 2017-07-11
    Statistics as Inductive Inference.Jan-Willem Romeijn - unknown
    An inductive logic is a system of inference that describes the relation between propositions on data, and propositions that extend beyond the data, such as predictions over future data, and general conclusions on all possible data. Statistics, on the other hand, is a mathematical discipline that describes procedures for deriving results about a population from sample data. These results include predictions on future samples, decisions on rejecting or accepting a hypothesis about the population, the determination of probability assignments over such (...)
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  32. added 2017-03-16
    A Critical Examination of the Analysis of Dichotomous Data.William H. Batchelder & Louis Narens - 1977 - Philosophy of Science 44 (1):113-135.
    This paper takes a critical look at theory-free, statistical methodologies for processing and interpreting data taken from respondents answering a set of dichotomous (yes-no) questions. The basic issue concerns to what extent theoretical conclusions based on such analyses are invariant under a class of "informationally equivalent" question transformations. First the notion of Boolean equivalence of two question sets is discussed. Then Lazarsfeld's latent structure analysis is considered in detail. It is discovered that the best fitting latent model depends on which (...)
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  33. added 2017-02-01
    Probabilistic Opinion Pooling with Imprecise Probabilities.Rush T. Stewart & Ignacio Ojea Quintana - 2018 - Journal of Philosophical Logic 47 (1):17-45.
    The question of how the probabilistic opinions of different individuals should be aggregated to form a group opinion is controversial. But one assumption seems to be pretty much common ground: for a group of Bayesians, the representation of group opinion should itself be a unique probability distribution, 410–414, [45]; Bordley Management Science, 28, 1137–1148, [5]; Genest et al. The Annals of Statistics, 487–501, [21]; Genest and Zidek Statistical Science, 114–135, [23]; Mongin Journal of Economic Theory, 66, 313–351, [46]; Clemen and (...)
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  34. added 2016-12-18
    Philosophical Aspect of Statistical Theory.C. West Churchman - 1946 - Philosophical Review 55 (1):81-87.
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  35. added 2016-12-08
    Significance Testing with No Alternative Hypothesis: A Measure of Surprise.J. V. Howard - 2009 - Erkenntnis 70 (2):253-270.
    A pure significance test would check the agreement of a statistical model with the observed data even when no alternative model was available. The paper proposes the use of a modified p -value to make such a test. The model will be rejected if something surprising is observed. It is shown that the relation between this measure of surprise and the surprise indices of Weaver and Good is similar to the relationship between a p -value, a corresponding odds-ratio, and a (...)
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  36. added 2016-12-05
    Can the Behavioral Sciences Self-Correct? A Social Epistemic Study.Felipe Romero - 2016 - Studies in History and Philosophy of Science Part A 60:55-69.
    Advocates of the self-corrective thesis argue that scientific method will refute false theories and find closer approximations to the truth in the long run. I discuss a contemporary interpretation of this thesis in terms of frequentist statistics in the context of the behavioral sciences. First, I identify experimental replications and systematic aggregation of evidence (meta-analysis) as the self-corrective mechanism. Then, I present a computer simulation study of scientific communities that implement this mechanism to argue that frequentist statistics may converge upon (...)
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  37. added 2016-12-05
    Probability All the Way Up.David Atkinson & Jeanne Peijnenburg - 2006 - Synthese 153 (2):187-197.
    Richard Jeffrey’s radical probabilism (‘probability all the way down’) is augmented by the claim that probability cannot be turned into certainty, except by data that logically exclude all alternatives. Once we start being uncertain, no amount of updating will free us from the treadmill of uncertainty. This claim is cast first in objectivist and then in subjectivist terms.
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  38. added 2016-11-04
    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. It is (...)
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  39. added 2016-10-13
    The Undetectable Difference: An Experimental Look at the ‘Problem’ of P-Values.William M. Goodman - 2010 - Statistical Literacy Website/Papers: Www.Statlit.Org/Pdf/2010GoodmanASA.Pdf.
    In the face of continuing assumptions by many scientists and journal editors that p-values provide a gold standard for inference, counter warnings are published periodically. But the core problem is not with p-values, per se. A finding that “p-value is less than α” could merely signal that a critical value has been exceeded. The question is why, when estimating a parameter, we provide a range (a confidence interval), but when testing a hypothesis about a parameter (e.g. µ = x) we (...)
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  40. added 2016-08-29
    A Philosophical Guide to Chance.J. T. M. Miller - 2016 - Philosophical Quarterly 66 (262):pqv037.
    A review of A Philosophical Guide to Chance by Toby Handfield. Cambridge CUP 2012.
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  41. added 2016-08-08
    Hypothesis Testing, “Dutch Book” Arguments, and Risk.Daniel Malinsky - 2015 - Philosophy of Science 82 (5):917-929.
    “Dutch Book” arguments and references to gambling theorems are typical in the debate between Bayesians and scientists committed to “classical” statistical methods. These arguments have rarely convinced non-Bayesian scientists to abandon certain conventional practices, partially because many scientists feel that gambling theorems have little relevance to their research activities. In other words, scientists “don’t bet.” This article examines one attempt, by Schervish, Seidenfeld, and Kadane, to progress beyond such apparent stalemates by connecting “Dutch Book”–type mathematical results with principles actually endorsed (...)
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  42. added 2016-04-19
    The Psychology of The Two Envelope Problem.J. S. Markovitch - manuscript
    This article concerns the psychology of the paradoxical Two Envelope Problem. The goal is to find instructive variants of the envelope switching problem that are capable of clear-cut resolution, while still retaining paradoxical features. By relocating the original problem into different contexts involving commutes and playing cards the reader is presented with a succession of resolved paradoxes that reduce the confusion arising from the parent paradox. The goal is to reduce confusion by understanding how we sometimes misread mathematical statements; or, (...)
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  43. added 2016-03-31
    Objective Evidence and Absence: Comment on Sober.Michael Strevens - 2009 - Philosophical Studies 143 (1):91 - 100.
    Elliott Sober argues that the statistical slogan “Absence of evidence is not evidence of absence” cannot be taken literally: it must be interpreted charitably as claiming that the absence of evidence is (typically) not very much evidence of absence. I offer an alternative interpretation, on which the slogan claims that absence of evidence is (typically) not objective evidence of absence. I sketch a definition of objective evidence, founded in the notion of an epistemically objective likelihood, and I show that in (...)
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  44. added 2015-11-03
    Philosophy as Conceptual Engineering: Inductive Logic in Rudolf Carnap's Scientific Philosophy.Christopher F. French - 2015 - Dissertation, University of British Columbia
    My dissertation explores the ways in which Rudolf Carnap sought to make philosophy scientific by further developing recent interpretive efforts to explain Carnap’s mature philosophical work as a form of engineering. It does this by looking in detail at his philosophical practice in his most sustained mature project, his work on pure and applied inductive logic. I, first, specify the sort of engineering Carnap is engaged in as involving an engineering design problem and then draw out the complications of design (...)
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  45. added 2015-09-17
    Compte rendu de « Desrosières, Alain (2014), Prouver et gouverner. Une analyse politique des statistiques publiques ». [REVIEW]Marc-Kevin Daoust - 2014 - Science Ouverte 1:1-7.
    Prouver et gouverner étudie le rôle des institutions, des conventions et des enjeux normatifs dans la construction d’indicateurs quantitatifs. Desrosières pense qu’on ne peut étudier le développement scientifique des statistiques sans prendre en compte le développement institutionnel – en particulier le rôle de l’État – dans la constitution de cette discipline.
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  46. added 2015-07-10
    On the Correct Interpretation of P Values and the Importance of Random Variables.Guillaume Rochefort-Maranda - 2016 - Synthese 193 (6):1777-1793.
    The p value is the probability under the null hypothesis of obtaining an experimental result that is at least as extreme as the one that we have actually obtained. That probability plays a crucial role in frequentist statistical inferences. But if we take the word ‘extreme’ to mean ‘improbable’, then we can show that this type of inference can be very problematic. In this paper, I argue that it is a mistake to make such an interpretation. Under minimal assumptions about (...)
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  47. added 2015-04-08
    Notes on Statistical Tests.Daniel Osherson - manuscript
    Let an unbiased coin be used to form an ω-sequence S of independent tosses. Let N be the positive integers. The finite initial segment of length n ∈ N is denoted by Sn (thus, S1 holds exactly the first toss). For n ∈ N , let Hn be the proportion of heads that show up in Sn.
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  48. added 2015-04-08
    Kettlewell From an Error Statisticians's Point of View.David Wÿss Rudge - 2001 - Perspectives on Science 9 (1):59-77.
    : Bayesians and error statisticians have relied heavily upon examples from physics in developing their accounts of scientific inference. The present essay demonstrates it is possible to analyze H.B.D. Kettlewell's classic study of natural selection from Deborah Mayo's error statistical point of view (Mayo 1996). A comparison with a previous analysis of this episode from a Bayesian perspective (Rudge 1998) reveals that the error statistical account makes better sense of investigations such as Kettlewell's because it clarifies how core elements in (...)
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  49. added 2015-04-05
    Critical Analysis of Ian Hacking's Book "Logic of Statistical Inference".Amato Herzel, Giampiero Landenna, Ian Società Italiana di Statistica & Hacking - 1982 - Stampato Col Contributo Del Consiglio Nazionale Delle Ricerche.
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  50. added 2015-03-22
    Foundations of Rational Choice Under Risk.Paul Anand - 1993 - Oxford University Press.
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