Results for 'Bayesian statistical method'

979 found
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  1. 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 (...)
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  2. (1 other version)Bayesian statistics in radiocarbon calibration.Daniel Steel - 2001 - Proceedings of the Philosophy of Science Association 2001 (3):S153-.
    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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  3.  71
    Bayesian statistics and biased procedures.Ronald N. Giere - 1969 - Synthese 20 (3):371 - 387.
    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 (...)
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  4.  67
    Bayeswatch: an overview of Bayesian statistics.Peter C. Austin, Lawrence J. Brunner & S. M. Janet E. Hux Md - 2002 - Journal of Evaluation in Clinical Practice 8 (2):277-286.
    Increasingly, clinical research is evaluated on the quality of its statistical analysis. Traditionally, statistical analyses in clinical research have been carried out from a ‘frequentist’ perspective. The presence of an alternative paradigm – the Bayesian paradigm – has been relatively unknown in clinical research until recently. There is currently a growing interest in the use of Bayesian statistics in health care research. This is due both to a growing realization of the limitations of frequentist methods and (...)
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  5. How Bayesian statistics are needed to determine whether mental states are unconscious.Zoltan Dienes - 2015 - In Morten Overgaard, Behavioral Methods in Consciousness Research. Oxford, United Kingdom: Oxford University Press.
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  6.  89
    Collectivist Foundations for Bayesian Statistics.Conor Mayo-Wilson & Aditya Saraf - unknown
    What justifies the use of Bayesian statistics in science? The traditional answer is that Bayesian statistics is simply an instance of orthodox expected utility theory. Thus, Bayesian statistical methods, like principles of utility theory, are justified by norms of individual rationality. In particular, most Bayesians argue that a scientist's credences must satisfy the probability axioms if she adheres to norms of practical and epistemic rationality. We argue that, to justify Bayesian statistics as a tool for (...)
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  7. Optimum Inductive Methods: A Study in Inductive Probability, Bayesian Statistics, and Verisimilitude.Roberto Festa - 1993 - Dordrecht, Netherland: Kluwer Academic Publishers: Dordrecht.
    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.  16
    The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App.Sarah Depaoli, Sonja D. Winter & Marieke Visser - 2020 - Frontiers in Psychology 11.
    The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a small (...)
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  9. Null-hypothesis tests are not completely stupid, but bayesian statistics are better.David Rindskopf - 1998 - Behavioral and Brain Sciences 21 (2):215-216.
    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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  10.  74
    A Battle in the Statistics Wars: a simulation-based comparison of Bayesian, Frequentist and Williamsonian methodologies.Mantas Radzvilas, William Peden & Francesco De Pretis - 2021 - Synthese 199 (5-6):13689-13748.
    The debates between Bayesian, frequentist, and other methodologies of statistics have tended to focus on conceptual justifications, sociological arguments, or mathematical proofs of their long run properties. Both Bayesian statistics and frequentist (“classical”) statistics have strong cases on these grounds. In this article, we instead approach the debates in the “Statistics Wars” from a largely unexplored angle: simulations of different methodologies’ performance in the short to medium run. We conducted a large number of simulations using a straightforward decision (...)
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  11. Error Statistics Using the Akaike and Bayesian Information Criteria.Henrique Cheng & Beckett Sterner - forthcoming - Erkenntnis.
    Many biologists, especially in ecology and evolution, analyze their data by estimating fits to a set of candidate models and selecting the best model according to the Akaike Information Criterion (AIC) or the Bayesian Information Criteria (BIC). When the candidate models represent alternative hypotheses, biologists may want to limit the chance of a false positive to a specified level. Existing model selection methodology, however, allows for only indirect control over error rates by setting a threshold for the difference in (...)
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  12.  8
    Bayesians Versus Frequentists: A Philosophical Debate on Statistical Reasoning.Jordi Vallverdú - 2016 - Berlin, Heidelberg: Imprint: Springer.
    This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Bayesian and frequentist approaches are subjected to a historical, cognitive and epistemological analysis, making it possible to not only compare the two competing theories, but to also find a potential solution. The work pursues a naturalistic approach, proceeding from the existence of numerosity in natural environments to the existence of contemporary formulas and methodologies to heuristic pragmatism, a (...)
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  13. Statistical Inference and the Replication Crisis.Lincoln J. Colling & Dénes Szűcs - 2018 - Review of Philosophy and Psychology 12 (1):121-147.
    The replication crisis has prompted many to call for statistical reform within the psychological sciences. Here we examine issues within Frequentist statistics that may have led to the replication crisis, and we examine the alternative—Bayesian statistics—that many have suggested as a replacement. The Frequentist approach and the Bayesian approach offer radically different perspectives on evidence and inference with the Frequentist approach prioritising error control and the Bayesian approach offering a formal method for quantifying the relative (...)
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  14.  27
    Thinking about statistics.Jun Otsuka - 2023 - Routledge.
    This article explores the intersection of philosophy and statistics by examining the philosophical assumptions underlying modern mathematical statistics from ontological and epistemological perspectives. Statistics holds interest for philosophers engaged with the problem of induction, as its mathematical apparatus serves as models for philosophical ideas. For instance, the much-discussed concepts of the uniformity of nature and natural kinds correspond to probability models and statistical models, which are fundamental to various statistical methods. Similarly, Dennett’s concept of a real pattern echoes (...)
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  15. The Statistical Riddle of Induction.Eric Johannesson - 2023 - Australasian Journal of Philosophy 101 (2):313-326.
    With his new riddle of induction, Goodman raised a problem for enumerative induction which many have taken to show that only some ‘natural’ properties can be used for making inductive inferences. Arguably, however, (i) enumerative induction is not a method that scientists use for making inductive inferences in the first place. Moreover, it seems at first sight that (ii) Goodman’s problem does not affect the method that scientists actually use for making such inferences—namely, classical statistics. Taken together, this (...)
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  16. Pragmatic warrant for frequentist statistical practice: the case of high energy physics.Kent W. 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 (...)
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  17.  50
    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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  18.  36
    On the Value of P Value: Toward Improving Statistical and Translational Significance— and Value—in Studies and the Applicability of Neurotechnologies for Precision Medicine.Raagasri Agraharam & James Giordano - 2018 - Ethics in Biology, Engineering and Medicine 9 (1):17-20.
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  19.  34
    The Philosophy of Quantitative Methods: Understanding Statistics.Brian D. Haig - 2018 - Oup Usa.
    The Philosophy of Quantitative Methods undertakes a philosophical examination of a number of important quantitative research methods within the behavioral sciences in order to overcome the non-critical approaches typically provided by textbooks. These research methods are exploratory data analysis, statistical significance testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. Further readings are provided to extend the reader's overall understanding of these methods.
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  20.  26
    Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT–R.Rhiannon Weaver - 2008 - Cognitive Science 32 (8):1349-1375.
    Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a “model fixed, data variable” paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood‐based, “data fixed, model variable” paradigm in which models (...)
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  21. bayesvl: Visually learning the graphical structure of Bayesian networks and performing MCMC with ‘Stan’.Viet-Phuong La & Quan-Hoang Vuong - 2019 - Vienna, Austria: The Comprehensive R Archive Network (CRAN).
    La, V. P., & Vuong, Q. H. (2019). bayesvl: Visually learning the graphical structure of Bayesian networks and performing MCMC with ‘Stan’. The Comprehensive R Archive Network (CRAN).
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  22.  97
    A method for explaining Bayesian networks for legal evidence with scenarios.Charlotte S. Vlek, Henry Prakken, Silja Renooij & Bart Verheij - 2016 - Artificial Intelligence and Law 24 (3):285-324.
    In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a (...)
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  23.  52
    Seeking Temporal Predictability in Speech: Comparing Statistical Approaches on 18 World Languages.Yannick Jadoul, Andrea Ravignani, Bill Thompson, Piera Filippi & Bart de Boer - 2016 - Frontiers in Human Neuroscience 10:196337.
    Temporal regularities in speech, such as interdependencies in the timing of speech events, are thought to scaffold early acquisition of the building blocks in speech. By providing on-line clues to the location and duration of upcoming syllables, temporal structure may aid segmentation and clustering of continuous speech into separable units. This hypothesis tacitly assumes that learners exploit predictability in the temporal structure of speech. Existing measures of speech timing tend to focus on first-order regularities among adjacent units, and are overly (...)
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  24.  28
    Frequentist statistical inference without repeated sampling.Paul Vos & Don Holbert - 2022 - Synthese 200 (2):1-25.
    Frequentist inference typically is described in terms of hypothetical repeated sampling but there are advantages to an interpretation that uses a single random sample. Contemporary examples are given that indicate probabilities for random phenomena are interpreted as classical probabilities, and this interpretation of equally likely chance outcomes is applied to statistical inference using urn models. These are used to address Bayesian criticisms of frequentist methods. Recent descriptions of p-values, confidence intervals, and power are viewed through the lens of (...)
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  25.  71
    A bayesian way to make stopping rules matter.Daniel Steel - 2003 - Erkenntnis 58 (2):213--227.
    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 (...)
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  26.  43
    The Statistical Philosophy of High Energy Physics: Pragmatism.Kent Staley - unknown
    The recent discovery of a Higgs boson prompted increased attention of statisticians and philosophers of science to the statistical methodology of High Energy Physics. Amidst long-standing debates within the field, HEP has adopted a mixed statistical methodology drawing upon both frequentist and Bayesian methods, but with standard frequentist techniques such as significance testing and confidence interval estimation playing a primary role. Physicists within HEP typically deny that their methodological decisions are guided by philosophical convictions, but are instead (...)
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  27.  23
    How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations.Katharina Böcherer-Linder & Andreas Eichler - 2019 - Frontiers in Psychology 10:375260.
    Bayes’ formula is a fundamental statistical method for inference judgments in uncertain situations used by both laymen and professionals. However, since people often fail in situations where Bayes’ formula can be applied, how to improve their performance in Bayesian situations is a crucial question. We based our research on a widely accepted beneficial strategy in Bayesian situations, representing the statistical information in the form of natural frequencies. In addition to this numerical format, we used five (...)
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  28.  32
    The Ethics of Randomised Controlled Trials: A Matter of Statistical Belief?Jane L. Hutton - 1996 - Health Care Analysis 4 (2):95-102.
    This paper outlines the approaches of two apparently competing schools of statistics. The criticisms made by supporters of Bayesian statistics about conventional Frequentist statistics are explained, and the Bayesian claim that their method enables research into new treatments without the need for clinical trials is examined in detail. Several further important issues are considered, including: the use of historical controls and data routinely collected on patients; balance in randomised trials; the possibility of giving information to patients; patient (...)
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  29.  71
    Decisions as statistical evidence and Birnbaum's 'confidence concept'.John W. Pratt - 1977 - Synthese 36 (1):59 - 69.
    To whatever extent the use of a behavioral, not an evidential, interpretation of decisions in the Lindley-Savage argument for Bayesian theory undermines its cogency as a criticism of typical standard practice, it also undermines the Neyman-Pearson theory as a support for typical standard practice. This leaves standard practice with far less theoretical support than Bayesian methods. It does nothing to resolve the anomalies and paradoxes of standard methods. (Similar statements apply to the common protestation that the models are (...)
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  30. Early stopping of RCTs: two potential issues for error statistics.Roger Stanev - 2015 - Synthese 192 (4):1089-1116.
    Error statistics is an important methodological view in philosophy of statistics and philosophy of science that can be applied to scientific experiments such as clinical trials. In this paper, I raise two potential issues for ES when it comes to guiding, and explaining early stopping of randomized controlled trials : ES provides limited guidance in cases of early unfavorable trends due to the possibility of trend reversal; ES is silent on how to prospectively control error rates in experiments requiring multiple (...)
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  31.  17
    An Improved Parameter-Estimating Method in Bayesian Networks Applied for Cognitive Diagnosis Assessment.Ling Ling Wang, Tao Xin & Liu Yanlou - 2021 - Frontiers in Psychology 12.
    Bayesian networks can be employed to cognitive diagnostic assessment. Most of the existing researches on the BNs for CDA utilized the MCMC algorithm to estimate parameters of BNs. When EM algorithm and gradient descending learning method are adopted to estimate the parameters of BNs, some challenges may emerge in educational assessment due to the monotonic constraints cannot be satisfied in the above two methods. This paper proposed to train the BN first based on the ideal response pattern data (...)
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  32. Cultural evolution in Vietnam’s early 20th century: a Bayesian networks analysis of Hanoi Franco-Chinese house designs.Quan-Hoang Vuong, Quang-Khiem Bui, Viet-Phuong La, Thu-Trang Vuong, Manh-Toan Ho, Hong-Kong T. Nguyen, Hong-Ngoc Nguyen, Kien-Cuong P. Nghiem & Manh-Tung Ho - 2019 - Social Sciences and Humanities Open 1 (1):100001.
    The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s Old (...)
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  33. A comparison of a Bayesian vs. a frequentist method for profiling hospital performance.Peter C. Austin, C. David Naylor & Jack V. Tu - 2001 - Journal of Evaluation in Clinical Practice 7 (1):35-45.
  34. Jacob's Ladder and Scientific Ontologies.Julio Michael Stern - 2014 - Cybernetics and Human Knowing 21 (3):9-43.
    The main goal of this article is to use the epistemological framework of a specific version of Cognitive Constructivism to address Piaget’s central problem of knowledge construction, namely, the re-equilibration of cognitive structures. The distinctive objective character of this constructivist framework is supported by formal inference methods of Bayesian statistics, and is based on Heinz von Foerster’s fundamental metaphor of objects as tokens for eigen-solutions. This epistemological perspective is illustrated using some episodes in the history of chemistry concerning the (...)
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  35. Constructive Verification, Empirical Induction, and Falibilist Deduction: A Threefold Contrast.Julio Michael Stern - 2011 - Information 2 (4):635-650.
    This article explores some open questions related to the problem of verification of theories in the context of empirical sciences by contrasting three epistemological frameworks. Each of these epistemological frameworks is based on a corresponding central metaphor, namely: (a) Neo-empiricism and the gambling metaphor; (b) Popperian falsificationism and the scientific tribunal metaphor; (c) Cognitive constructivism and the object as eigen-solution metaphor. Each of one of these epistemological frameworks has also historically co-evolved with a certain statistical theory and method (...)
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  36.  51
    Inductive strategy and statistical tactics.Paul Snow - 1998 - Behavioral and Brain Sciences 21 (2):219-219.
    Chow ably defends classical significance testing by relating this method to venerable principles for inductive reasoning. Chow's success does not preclude the use of other approaches to statistical reasoning, which is fortunate not only for Bayesian rivals, but even for some fellow classicists.
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  37. (1 other version)Bayesian Informal Logic and Fallacy.Kevin Korb - 2003 - Informal Logic 23 (1).
    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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  38.  43
    We are All Bayesian, Everyone is Not a Bayesian.Mattia Andreoletti & Andrea Oldofredi - 2019 - Topoi 38 (2):477-485.
    Medical research makes intensive use of statistics in order to support its claims. In this paper we make explicit an epistemological tension between the conduct of clinical trials and their interpretation: statistical evidence is sometimes discarded on the basis of an underlined Bayesian reasoning. We suggest that acknowledging the potentiality of Bayesian statistics might contribute to clarify and improve comprehension of medical research. Nevertheless, despite Bayesianism may provide a better account for scientific inference with respect to the (...)
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  39. Where do Bayesian priors come from?Patrick Suppes - 2007 - Synthese 156 (3):441-471.
    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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  40. 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 (...)
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  41.  33
    An Eye-Tracking Study of Statistical Reasoning With Tree Diagrams and 2 × 2 Tables.Georg Bruckmaier, Karin Binder, Stefan Krauss & Han-Min Kufner - 2019 - Frontiers in Psychology 10:436373.
    Changing the information format from probabilities into frequencies as well as employing appropriate visualizations such as tree diagrams or 2 × 2 tables are important tools that can facilitate people’s statistical reasoning. Previous studies have shown that despite their widespread use in statistical textbooks, both of those visualization types are only of restricted help when they are provided with probabilities, but that they can foster insight when presented with frequencies instead. In the present study, we attempt to replicate (...)
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  42.  24
    Bayesian ockham’s razor and nested models.Bengt Autzen - 2019 - Economics and Philosophy 35 (2):321-338.
    :While Bayesian methods are widely used in economics and finance, the foundations of this approach remain controversial. In the contemporary statistical literature Bayesian Ockham’s razor refers to the observation that the Bayesian approach to scientific inference will automatically assign greater likelihood to a simpler hypothesis if the data are compatible with both a simpler and a more complex hypothesis. In this paper I will discuss a problem that results when Bayesian Ockham’s razor is applied to (...)
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  43. A simple proof of Born’s rule for statistical interpretation of quantum mechanics.Biswaranjan Dikshit - 2017 - Journal for Foundations and Applications of Physics 4 (1):24-30.
    The Born’s rule to interpret the square of wave function as the probability to get a specific value in measurement has been accepted as a postulate in foundations of quantum mechanics. Although there have been so many attempts at deriving this rule theoretically using different approaches such as frequency operator approach, many-world theory, Bayesian probability and envariance, literature shows that arguments in each of these methods are circular. In view of absence of a convincing theoretical proof, recently some researchers (...)
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  44. Improved model exploration for the relationship between moral foundations and moral judgment development using Bayesian Model Averaging.Hyemin Han & Kelsie J. Dawson - 2022 - Journal of Moral Education 51 (2):204-218.
    Although some previous studies have investigated the relationship between moral foundations and moral judgment development, the methods used have not been able to fully explore the relationship. In the present study, we used Bayesian Model Averaging (BMA) in order to address the limitations in traditional regression methods that have been used previously. Results showed consistency with previous findings that binding foundations are negatively correlated with post-conventional moral reasoning and positively correlated with maintaining norms and personal interest schemas. In addition (...)
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  45.  74
    The evaluation of measurement uncertainties and its epistemological ramifications.Nadine de Courtenay & Fabien Grégis - 2017 - Studies in History and Philosophy of Science Part A 65:21-32.
    The way metrologists conceive of measurement has undergone a major shift in the last two decades. This shift can in great part be traced to a change in the statistical methods used to deal with the expression of measurement results, and, more particularly, with the calculation of measurement uncertainties. Indeed, as we show, the incapacity of the frequentist approach to the calculus of uncertainty to deal with systematic errors has prompted the replacement of the customary frequentist methods by fully (...)
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  46.  73
    Quantitative Standards for Absolute Linguistic Universals.Steven T. Piantadosi & Edward Gibson - 2014 - Cognitive Science 38 (4):736-756.
    Absolute linguistic universals are often justified by cross-linguistic analysis: If all observed languages exhibit a property, the property is taken to be a likely universal, perhaps specified in the cognitive or linguistic systems of language learners and users. In many cases, these patterns are then taken to motivate linguistic theory. Here, we show that cross-linguistic analysis will very rarely be able to statistically justify absolute, inviolable patterns in language. We formalize two statistical methods—frequentist and Bayesian—and show that in (...)
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  47.  40
    Experimental Investigation on the Elicitation of Subjective Distributions.Carlos J. Barrera-Causil, Juan Carlos Correa & Fernando Marmolejo-Ramos - 2019 - Frontiers in Psychology 10:423927.
    Elicitation methods aim to build participants' distributions about a parameter of interest. In most elicitation studies this parameter is rarely known in advance and hinders an objective comparison between elicitation methods. In two experiments, participants were first presented with a fixed random sequence of images and numbers and subsequently their subjective distributions of percentages of one of those numbers was elicited. Importantly, the true percentage was set in advance. The first experiment tested whether receiving instructions as to the elicitation (...) would assist in estimating a true value more accurately than receiving no instructions and whether accuracy was determined by the numerical skills of the participants. The second experiment sought to compare the elicitation method used in the first experiment with a variation of a graphical elicitation method. The results indicate that (i) receiving instructions as to the elicitation method does assist in producing estimates closer to a true percentage value, (ii) the level of numerical skills does not play a part in the accuracy of the estimation (Experiment 1), and (iii) although the average estimates of the betting and graphical method are not significantly different, the betting method leads to more precise estimations than the graphical method (Experiment 2). Both studies featured statistical procedures (functional data analysis and a novel clustering technique) not considered in past research on the elicitation of subjective distributions. The implications of these results are discussed in relation to a recent key study. (shrink)
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  48.  61
    Bayesian Rationality and Decision Making: A Critical Review.Max Albert - 2003 - Analyse & Kritik 25 (1):101-117.
    Bayesianism is the predominant philosophy of science in North-America, the most important school of statistics world-wide, and the general version of the rational-choice approach in the social sciences. Although often rejected as a theory of actual behavior, it is still the benchmark case of perfect rationality. The paper reviews the development of Bayesianism in philosophy, statistics and decision making and questions its status as an account of perfect rationality. Bayesians, who otherwise are squarely in the empiricist camp, invoke a priori (...)
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  49.  58
    The P–T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning.Chenguang Lu - 2020 - Philosophies 5 (4):25.
    Many researchers want to unify probability and logic by defining logical probability or probabilistic logic reasonably. This paper tries to unify statistics and logic so that we can use both statistical probability and logical probability at the same time. For this purpose, this paper proposes the P–T probability framework, which is assembled with Shannon’s statistical probability framework for communication, Kolmogorov’s probability axioms for logical probability, and Zadeh’s membership functions used as truth functions. Two kinds of probabilities are connected (...)
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    Instruction in information structuring improves Bayesian judgment in intelligence analysts.David R. Mandel - 2015 - Frontiers in Psychology 6:137593.
    An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts’ probability judgments were more coherent (i.e., more additive and compliant with Bayes theorem). Instruction also improved the coherence of binary choices regarding category membership: after instruction, subjects were more (...)
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