Results for 'Bayesian Inference'

999 found
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  1.  49
    Universal Bayesian Inference?David Dowe & Graham Oppy - 2001 - Behavioral and Brain Sciences 24 (4):662-663.
    We criticise Shepard's notions of “invariance” and “universality,” and the incorporation of Shepard's work on inference into the general framework of his paper. We then criticise Tenenbaum and Griffiths' account of Shepard (1987b), including the attributed likelihood function, and the assumption of “weak sampling.” Finally, we endorse Barlow's suggestion that minimum message length (MML) theory has useful things to say about the Bayesian inference problems discussed by Shepard and Tenenbaum and Griffiths. [Barlow; Shepard; Tenenbaum & Griffiths].
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  2. 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 (...)
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  3. A Dual Approach to Bayesian Inference and Adaptive Control.Leigh Tesfatsion - 1982 - Theory and Decision 14 (2):177-194.
    Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional probability assessments to the direct updating of the criterion function, itself, via transitional utility assessments. Results are illustrated in terms of an adaptive reinvestment two-armed bandit problem.
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  4.  20
    Decoupling, Sparsity, Randomization, and Objective Bayesian Inference.Julio Michael Stern - 2008 - Cybernetics and Human Knowing 15 (2):49-68..
    Decoupling is a general principle that allows us to separate simple components in a complex system. In statistics, decoupling is often expressed as independence, no association, or zero covariance relations. These relations are sharp statistical hypotheses, that can be tested using the FBST - Full Bayesian Significance Test. Decoupling relations can also be introduced by some techniques of Design of Statistical Experiments, DSEs, like randomization. This article discusses the concepts of decoupling, randomization and sparsely connected statistical models in the (...)
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  5. Semantic Information G Theory and Logical Bayesian Inference for Machine Learning.Chenguang Lu - 2019 - Information 10 (8):261.
    An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. MultilabelMultilabel A semantic channel in the G (...)
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  6. Explanatoriness is Evidentially Irrelevant, or Inference to the Best Explanation Meets Bayesian Confirmation Theory.W. Roche & E. Sober - 2013 - Analysis 73 (4):659-668.
    In the world of philosophy of science, the dominant theory of confirmation is Bayesian. In the wider philosophical world, the idea of inference to the best explanation exerts a considerable influence. Here we place the two worlds in collision, using Bayesian confirmation theory to argue that explanatoriness is evidentially irrelevant.
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  7. Inference to the Best Explanation Made Incoherent.Nevin Climenhaga - 2017 - Journal of Philosophy 114 (5):251-273.
    Defenders of Inference to the Best Explanation claim that explanatory factors should play an important role in empirical inference. They disagree, however, about how exactly to formulate this role. In particular, they disagree about whether to formulate IBE as an inference rule for full beliefs or for degrees of belief, as well as how a rule for degrees of belief should relate to Bayesianism. In this essay I advance a new argument against non-Bayesian versions of IBE. (...)
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  8.  71
    Refining the Bayesian Approach to Unifying Generalisation.Nina Poth - forthcoming - Review of Philosophy and Psychology:1-31.
    Tenenbaum and Griffiths (2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (1987) and Tversky’s (1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that generalisation as a Bayesian inference should be seen (...)
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  9. Bayesian Learning Models of Pain: A Call to Action.Abby Tabor & Christopher Burr - 2019 - Current Opinion in Behavioral Sciences 26:54-61.
    Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.
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  10. Literal Perceptual Inference.Alex Kiefer - 2017 - In Thomas Metzinger & Wanja Wiese (eds.), Philosophy and predictive processing. Frankfurt, Germany:
    In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse. -/- In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences (...)
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  11. 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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  12. Can There Be a Bayesian Explanationism? On the Prospects of a Productive Partnership.Frank Cabrera - 2017 - Synthese 194 (4):1245–1272.
    In this paper, I consider the relationship between Inference to the Best Explanation and Bayesianism, both of which are well-known accounts of the nature of scientific inference. In Sect. 2, I give a brief overview of Bayesianism and IBE. In Sect. 3, I argue that IBE in its most prominently defended forms is difficult to reconcile with Bayesianism because not all of the items that feature on popular lists of “explanatory virtues”—by means of which IBE ranks competing explanations—have (...)
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  13.  59
    A Bayesian Solution to Hallsson's Puzzle.Thomas Mulligan - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy:1-14.
    Politics is rife with motivated cognition. People do not dispassionately engage with the evidence when they form political beliefs; they interpret it selectively, generating justifications for their desired conclusions and reasons why contrary evidence should be ignored. Moreover, research shows that epistemic ability (e.g. intelligence and familiarity with evidence) is correlated with motivated cognition. Bjørn Hallsson has pointed out that this raises a puzzle for the epistemology of disagreement. On the one hand, we typically think that epistemic ability in an (...)
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  14. Bayesian Analysis in Social Sciences.Minh-Hoang Nguyen - 2021 - Scholarly Community Encyclopedia.
    Given the reproducibility crisis (or replication crisis), more psychologists and social-cultural scientists are getting involved with Bayesian inference. Therefore, the current article provides a brief overview of programs (or software) and steps to conduct Bayesian data analysis in social sciences.
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  15. The Bayesian Explanation of Transmission Failure.Geoff Pynn - 2013 - Synthese 190 (9):1519-1531.
    Even if our justified beliefs are closed under known entailment, there may still be instances of transmission failure. Transmission failure occurs when P entails Q, but a subject cannot acquire a justified belief that Q by deducing it from P. Paradigm cases of transmission failure involve inferences from mundane beliefs (e.g., that the wall in front of you is red) to the denials of skeptical hypotheses relative to those beliefs (e.g., that the wall in front of you is not white (...)
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  16.  35
    Cointegration: Bayesian Significance Test Communications in Statistics.Julio Michael Stern, Marcio Alves Diniz & Carlos Alberto de Braganca Pereira - 2012 - Communications in Statistics 41 (19):3562-3574.
    To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that “the topic of selecting the cointegrating rank has not yet given very useful and convincing results”. (...)
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  17.  70
    Bayesian Evidence Test for Precise Hypotheses.Julio Michael Stern - 2003 - Journal of Statistical Planning and Inference 117 (2):185-198.
    The full Bayesian signi/cance test (FBST) for precise hypotheses is presented, with some illustrative applications. In the FBST we compute the evidence against the precise hypothesis. We discuss some of the theoretical properties of the FBST, and provide an invariant formulation for coordinate transformations, provided a reference density has been established. This evidence is the probability of the highest relative surprise set, “tangential” to the sub-manifold (of the parameter space) that defines the null hypothesis.
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  18. Abductive Inference and Delusional Belief.Max Coltheart, Peter Menzies & John Sutton - 2010 - Cognitive Neuropsychiatry 15 (1):261-287.
    Delusional beliefs have sometimes been considered as rational inferences from abnormal experiences. We explore this idea in more detail, making the following points. Firstly, the abnormalities of cognition which initially prompt the entertaining of a delusional belief are not always conscious and since we prefer to restrict the term “experience” to consciousness we refer to “abnormal data” rather than “abnormal experience”. Secondly, we argue that in relation to many delusions (we consider eight) one can clearly identify what the abnormal cognitive (...)
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  19. Inference to the Best Explanation - An Overview.Frank Cabrera - 2022 - In Lorenzo Magnani (ed.), Handbook of Abductive Cognition. Cham: Springer. pp. 1-34.
    In this article, I will provide a critical overview of the form of non-deductive reasoning commonly known as “Inference to the Best Explanation” (IBE). Roughly speaking, according to IBE, we ought to infer the hypothesis that provides the best explanation of our evidence. In section 2, I survey some contemporary formulations of IBE and highlight some of its putative applications. In section 3, I distinguish IBE from C.S. Peirce’s notion of abduction. After underlining some of the essential elements of (...)
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  20.  56
    Causal Inference as Inference to the Best Explanation.Barry Ward - manuscript
    We argue that a modified version of Mill’s method of agreement can strongly confirm causal generalizations. This mode of causal inference implicates the explanatory virtues of mechanism, analogy, consilience, and simplicity, and we identify it as a species of Inference to the Best Explanation (IBE). Since rational causal inference provides normative guidance, IBE is not a heuristic for Bayesian rationality. We give it an objective Bayesian formalization, one that has no need of principles of indifference (...)
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  21. The Heuristic Conception of Inference to the Best Explanation.Finnur Dellsén - 2017 - Philosophical Studies 175 (7):1745-1766.
    An influential suggestion about the relationship between Bayesianism and inference to the best explanation holds that IBE functions as a heuristic to approximate Bayesian reasoning. While this view promises to unify Bayesianism and IBE in a very attractive manner, important elements of the view have not yet been spelled out in detail. I present and argue for a heuristic conception of IBE on which IBE serves primarily to locate the most probable available explanatory hypothesis to serve as a (...)
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  22.  54
    Unit Roots: Bayesian Significance Test.Julio Michael Stern, Marcio Alves Diniz & Carlos Alberto de Braganca Pereira - 2011 - Communications in Statistics 40 (23):4200-4213.
    The unit root problem plays a central role in empirical applications in the time series econometric literature. However, significance tests developed under the frequentist tradition present various conceptual problems that jeopardize the power of these tests, especially for small samples. Bayesian alternatives, although having interesting interpretations and being precisely defined, experience problems due to the fact that that the hypothesis of interest in this case is sharp or precise. The Bayesian significance test used in this article, for the (...)
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  23. Statistical Inference and the Replication Crisis.Lincoln J. Colling & Dénes Szűcs - 2021 - 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 strength (...)
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  24.  7
    Inference to the Best Explanation and Rejecting the Resurrection.David Kyle Johnson - 2021 - Socio-Historical Examination of Religion and Ministry 3 (1):26-51.
    Christian apologists, like Willian Lane Craig and Stephen T. Davis, argue that belief in Jesus’ resurrection is reasonable because it provides the best explanation of the available evidence. In this article, I refute that thesis. To do so, I lay out how the logic of inference to the best explanation (IBE) operates, including what good explanations must be and do by definition, and then apply IBE to the issue at hand. Multiple explanations—including (what I will call) The Resurrection Hypothesis, (...)
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  25. On the Pragmatic and Epistemic Virtues of Inference to the Best Explanation.Richard Pettigrew - 2021 - Synthese 199 (5-6):12407-12438.
    In a series of papers over the past twenty years, and in a new book, Igor Douven has argued that Bayesians are too quick to reject versions of inference to the best explanation that cannot be accommodated within their framework. In this paper, I survey their worries and attempt to answer them using a series of pragmatic and purely epistemic arguments that I take to show that Bayes’ Rule really is the only rational way to respond to your evidence.
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  26. The New Tweety Puzzle: Arguments Against Monistic Bayesian Approaches in Epistemology and Cognitive Science.Matthias Unterhuber & Gerhard Schurz - 2013 - Synthese 190 (8):1407-1435.
    In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...)
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  27. Statistical Inference and the Plethora of Probability Paradigms: A Principled Pluralism.Mark L. Taper, Gordon Brittan Jr & Prasanta S. Bandyopadhyay - manuscript
    The major competing statistical paradigms share a common remarkable but unremarked thread: in many of their inferential applications, different probability interpretations are combined. How this plays out in different theories of inference depends on the type of question asked. We distinguish four question types: confirmation, evidence, decision, and prediction. We show that Bayesian confirmation theory mixes what are intuitively “subjective” and “objective” interpretations of probability, whereas the likelihood-based account of evidence melds three conceptions of what constitutes an “objective” (...)
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  28.  28
    Genuine Bayesian Multiallelic Significance Test for the Hardy-Weinberg Equilibrium Law.Julio Michael Stern, Carlos Alberto de Braganca Pereira, Fabio Nakano & Martin Ritter Whittle - 2006 - Genetics and Molecular Research 5 (4):619-631.
    Statistical tests that detect and measure deviation from the Hardy-Weinberg equilibrium (HWE) have been devised but are limited when testing for deviation at multiallelic DNA loci is attempted. Here we present the full Bayesian significance test (FBST) for the HWE. This test depends neither on asymptotic results nor on the number of possible alleles for the particular locus being evaluated. The FBST is based on the computation of an evidence index in favor of the HWE hypothesis. A great deal (...)
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  29.  86
    Genericity and Inductive Inference.Henry Ian Schiller - forthcoming - Philosophy of Science.
    We are often justified in acting on the basis of evidential confirmation. I argue that such evidence supports belief in non-quantificational generic generalizations, rather than universally quantified generalizations. I show how this account supports, rather than undermines, a Bayesian account of confirmation. Induction from confirming instances of a generalization to belief in the corresponding generic is part of a reasoning instinct that is typically (but not always) correct, and allows us to approximate the predictions that formal epistemology would make.
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  30. On Probability and Cosmology: Inference Beyond Data?Martin Sahlen - 2017 - In K. Chamcham, J. Silk, J. D. Barrow & S. Saunders (eds.), The Philosophy of Cosmology. Cambridge, UK:
    Modern scientific cosmology pushes the boundaries of knowledge and the knowable. This is prompting questions on the nature of scientific knowledge. A central issue is what defines a 'good' model. When addressing global properties of the Universe or its initial state this becomes a particularly pressing issue. How to assess the probability of the Universe as a whole is empirically ambiguous, since we can examine only part of a single realisation of the system under investigation: at some point, data will (...)
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  31. Chains of Inferences and the New Paradigm in the Psychology of Reasoning.Ulf Hlobil - 2016 - Review of Philosophy and Psychology 7 (1):1-16.
    The new paradigm in the psychology of reasoning draws on Bayesian formal frameworks, and some advocates of the new paradigm think of these formal frameworks as providing a computational-level theory of rational human inference. I argue that Bayesian theories should not be seen as providing a computational-level theory of rational human inference, where by “Bayesian theories” I mean theories that claim that all rational credal states are probabilistically coherent and that rational adjustments of degrees of (...)
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  32.  58
    Factorization of Sparse Bayesian Networks.Julio Michael Stern & Ernesto Coutinho Colla - 2009 - Studies in Computational Intelligence 199:275-285.
    This paper shows how an efficient and parallel algorithm for inference in Bayesian Networks (BNs) can be built and implemented combining sparse matrix factorization methods with variable elimination algorithms for BNs. This entails a complete separation between a first symbolic phase, and a second numerical phase.
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  33.  59
    Can a Significance Test Be Genuinely Bayesian?Julio Michael Stern, Carlos Alberto de Braganca Pereira & Sergio Wechsler - 2008 - Bayesian Analysis 3 (1):79-100.
    The Full Bayesian Significance Test, FBST, is extensively reviewed. Its test statistic, a genuine Bayesian measure of evidence, is discussed in detail. Its behavior in some problems of statistical inference like testing for independence in contingency tables is discussed.
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  34. Resolving the Raven Paradox: Simple Random Sampling, Stratified Random Sampling, and Inference to Best Explanation.Barry Ward - 2022 - Philosophy of Science 89 (2):360-377.
    Simple random sampling resolutions of the raven paradox relevantly diverge from scientific practice. We develop a stratified random sampling model, yielding a better fit and apparently rehabilitating simple random sampling as a legitimate idealization. However, neither accommodates a second concern, the objection from potential bias. We develop a third model that crucially invokes causal considerations, yielding a novel resolution that handles both concerns. This approach resembles Inference to the Best Explanation (IBE) and relates the generalization’s confirmation to confirmation of (...)
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  35.  40
    Ideal Observers, Real Observers, and the Return of Elvis.Ronald A. Rensink - 1996 - In David Knill & Whitman Richards (eds.), Perception as Bayesian Inference. Cambridge University Press. pp. 451-455.
    Knill, Kersten, & Mamassian (Chapter 6) provide an interesting discussion of how the Bayesian formulation can be used to help investigate human vision. In their view, computational theories can be based on an ideal observer that uses Bayesian inference to make optimal use of available information. Four factors are important here: the image information used, the output structures estimated, the priors assumed (i.e., knowledge about the structure of the world), and the likelihood function used (i.e., knowledge about (...)
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  36. The Epistemology of Disagreement: Why Not Bayesianism?Thomas Mulligan - 2021 - Episteme 18 (4):587-602.
    Disagreement is a ubiquitous feature of human life, and philosophers have dutifully attended to it. One important question related to disagreement is epistemological: How does a rational person change her beliefs (if at all) in light of disagreement from others? The typical methodology for answering this question is to endorse a steadfast or conciliatory disagreement norm (and not both) on a priori grounds and selected intuitive cases. In this paper, I argue that this methodology is misguided. Instead, a thoroughgoingly (...) strategy is what's needed. Such a strategy provides conciliatory norms in appropriate cases and steadfast norms in appropriate cases. I argue, further, that the few extant efforts to address disagreement in the Bayesian spirit are laudable but uncompelling. A modelling, rather than a functional, approach gets us the right norms and is highly general, allowing the epistemologist to deal with (1) multiple epistemic interlocutors, (2) epistemic superiors and inferiors (i.e. not just epistemic peers), and (3) dependence between interlocutors. (shrink)
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  37. Vanilla PP for Philosophers: A Primer on Predictive Processing.Wanja Wiese & Thomas Metzinger - 2017 - Philosophy and Predictive Processing.
    The goal of this short chapter, aimed at philosophers, is to provide an overview and brief explanation of some central concepts involved in predictive processing (PP). Even those who consider themselves experts on the topic may find it helpful to see how the central terms are used in this collection. To keep things simple, we will first informally define a set of features important to predictive processing, supplemented by some short explanations and an alphabetic glossary. -/- The features described here (...)
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  38. On the Role of Explanatory and Systematic Power in Scientific Reasoning.Peter Brössel - 2015 - Synthese 192 (12):3877-3913.
    The paper investigates measures of explanatory power and how to define the inference schema “Inference to the Best Explanation”. It argues that these measures can also be used to quantify the systematic power of a hypothesis and the inference schema “Inference to the Best Systematization” is defined. It demonstrates that systematic power is a fruitful criterion for theory choice and IBS is truth-conducive. It also shows that even radical Bayesians must admit that systemic power is an (...)
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  39. FBST for a Generalized Poisson Distribution.Julio Michael Stern, Paulo do Canto Hubert & Marcelo de Souza Lauretto - 2009 - AIP Conference Proceedings 1193:210-217.
    The Generalized Poisson Distribution (GPD) adds an extra parameter to the usual Poisson distribution. This parameter induces a loss of homogeneity in the stochastic processes modeled by the distribution. Thus, the generalized distribution becomes an useful model for counting processes where the occurrence of events is not homogeneous. This model creates the need for an inferential procedure, to test for the value of this extra parameter. The FBST (Full Bayesian Significance Test) is a Bayesian hypotheses test procedure, capable (...)
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  40. Low Attention Impairs Optimal Incorporation of Prior Knowledge in Perceptual Decisions.Jorge Morales, Guillermo Solovey, Brian Maniscalco, Dobromir Rahnev, Floris P. de Lange & Hakwan Lau - 2015 - Attention, Perception, and Psychophysics 77 (6):2021-2036.
    When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the (...)
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  41. Beyond Falsifiability: Normal Science in a Multiverse.Sean M. Carroll - 2019 - In Richard Dawid, Radin Dardashti & Karim Thebault (eds.), Epistemology of Fundamental Physics: Why Trust a Theory? Cambridge, UK: Cambridge University Press.
    Cosmological models that invoke a multiverse - a collection of unobservable regions of space where conditions are very different from the region around us - are controversial, on the grounds that unobservable phenomena shouldn't play a crucial role in legitimate scientific theories. I argue that the way we evaluate multiverse models is precisely the same as the way we evaluate any other models, on the basis of abduction, Bayesian inference, and empirical success. There is no scientifically respectable way (...)
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  42. Experiential Fantasies, Prediction, and Enactive Minds.Michael David Kirchhoff - 2015 - Journal of Consciousness Studies 22 (3-4):68-92.
    A recent surge of work on prediction-driven processing models--based on Bayesian inference and representation-heavy models--suggests that the material basis of conscious experience is inferentially secluded and neurocentrically brain bound. This paper develops an alternative account based on the free energy principle. It is argued that the free energy principle provides the right basic tools for understanding the anticipatory dynamics of the brain within a larger brain-body-environment dynamic, viewing the material basis of some conscious experiences as extensive--relational and thoroughly (...)
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  43.  69
    The Literalist Fallacy & the Free Energy Principle: Model Building, Scientific Realism and Instrumentalism.Michael David Kirchhoff, Julian Kiverstein & Ian Robertson - manuscript
    Disagreement about how best to think of the relation between theories and the realities they represent has a longstanding and venerable history. We take up this debate in relation to the free energy principle (FEP) - a contemporary framework in computational neuroscience, theoretical biology and the philosophy of cognitive science. The FEP is very ambitious, extending from the brain sciences to the biology of self-organisation. In this context, some find apparent discrepancies between the map (the FEP) and the territory (target (...)
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  44. Fragmentation and Old Evidence.Will Fleisher - forthcoming - Episteme:1-26.
    Bayesian confirmation theory is our best formal framework for describing inductive reasoning. The problem of old evidence is a particularly difficult one for confirmation theory, because it suggests that this framework fails to account for central and important cases of inductive reasoning and scientific inference. I show that we can appeal to the fragmentation of doxastic states to solve this problem for confirmation theory. This fragmentation solution is independently well-motivated because of the success of fragmentation in solving other (...)
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  45.  37
    Are Generative Models Structural Representations?Marco Facchin - 2021 - Minds and Machines 31 (2):277-303.
    Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine the only (...)
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  46. Cerebellum and Emotion in Morality.Hyemin Han - forthcoming - In Michael Adamaszek, Mario Manto & Denis Schutter (eds.), Cerebellum and Emotion.
    In the current chapter, I examined the relationship between the cerebellum, emotion, and morality with evidence from large-scale neuroimaging data analysis. Although the aforementioned relationship has not been well studied in neuroscience, recent studies have shown that the cerebellum is closely associated with emotional and social processes at the neural level. Also, debates in the field of moral philosophy, psychology, and neuroscience have supported the importance of emotion in moral functioning. Thus, I explored the potentially important but less-studies topic with (...)
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  47. Why Do We Need Perceptual Content?Ayoob Shahmoradi - 2016 - Philosophical Psychology 29 (5):776-788.
    Most representationalists argue that perceptual experience has to be representational because phenomenal looks are, by themselves, representational. Charles Travis argues that looks cannot represent. I argue that perceptual experience has to be representational due to the way the visual system works.
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  48. Sudden Infant Death or Murder? A Royal Confusion About Probabilities.Neven Sesardic - 2007 - British Journal for the Philosophy of Science 58 (2):299-329.
    In this article I criticize the recommendations of some prominent statisticians about how to estimate and compare probabilities of the repeated sudden infant death and repeated murder. The issue has drawn considerable public attention in connection with several recent court cases in the UK. I try to show that when the three components of the Bayesian inference are carefully analyzed in this context, the advice of the statisticians turns out to be problematic in each of the steps.
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    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 (...)
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  50. Learning Concepts: A Learning-Theoretic Solution to the Complex-First Paradox.Nina Laura Poth & Peter Brössel - 2020 - Philosophy of Science 87 (1):135-151.
    Children acquire complex concepts like DOG earlier than simple concepts like BROWN, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Werning 2010). This is the Complex- First Paradox. We present a novel solution to the Complex-First Paradox. Our solution builds on a generalization of Xu and Tenenbaum’s (2007) Bayesian model of word learning. By focusing on a rational theory of concept learning, we show that (...)
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