Results for 'Probabilism'

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  1. Anne M. Fagot.Some Shortcomings of A. Probabilistic - 1984 - In Lennart Nordenfelt & B. I. B. Lindahl (eds.), Health, Disease, and Causal Explanations in Medicine. Reidel. pp. 101.
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  2. James H. Fetzer.Probabilistic Metaphysics - 1988 - In J. Fetzer (ed.), Probability and Causality. D. Reidel. pp. 192--109.
  3.  11
    Hector freytes, Antonio ledda, Giuseppe sergioli and.Roberto Giuntini & Probabilistic Logics in Quantum Computation - 2013 - In Hanne Andersen, Dennis Dieks, Wenceslao González, Thomas Uebel & Gregory Wheeler (eds.), New Challenges to Philosophy of Science. Springer Verlag. pp. 49.
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  4.  69
    Probabilistic Knowledge.Sarah Moss - 2018 - Oxford, United Kingdom: Oxford University Press.
    Traditional philosophical discussions of knowledge have focused on the epistemic status of full beliefs. In this book, Moss argues that in addition to full beliefs, credences can constitute knowledge. For instance, your .4 credence that it is raining outside can constitute knowledge, in just the same way that your full beliefs can. In addition, you can know that it might be raining, and that if it is raining then it is probably cloudy, where this knowledge is not knowledge of propositions, (...)
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  5. Probabilistic Arguments in the Epistemological Approach to Argumentation.Christoph Lumer - 2011 - In Frans H. Van Eemeren, Bart Garssen, David Godden & Gordon Mitchell (eds.), Proceedings of the 7th Conference of the International Society for the Study of Argumentation. Amsterdam, Netherlands: Rozenberg; Sic Sat. pp. 1141-1154.
    The aim of the paper is to develop general criteria of argumentative validity and adequacy for probabilistic arguments on the basis of the epistemological approach to argumentation. In this approach, as in most other approaches to argumentation, proabilistic arguments have been neglected somewhat. Nonetheless, criteria for several special types of probabilistic arguments have been developed, in particular by Richard Feldman and Christoph Lumer. In the first part (sects. 2-5) the epistemological basis of probabilistic arguments is discussed. With regard to the (...)
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    The Probabilistic Mind: Prospects for Bayesian Cognitive Science.Nick Chater & Mike Oaksford (eds.) - 2008 - Oxford University Press.
    'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition'. It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods.
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  7. Probabilistic Causality.Ellery Eells - 1991 - Cambridge, England: Cambridge University Press.
    In this important book, Ellery Eells explores and refines philosophical conceptions of probabilistic causality. In a probabilistic theory of causation, causes increase the probability of their effects rather than necessitate their effects in the ways traditional deterministic theories have specified. Philosophical interest in this subject arises from attempts to understand population sciences as well as indeterminism in physics. Taking into account issues involving spurious correlation, probabilistic causal interaction, disjunctive causal factors, and temporal ideas, Professor Eells advances the analysis of what (...)
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  8. Context Probabilism.Seth Yalcin - 2012 - In M. Aloni, Maria Aloni, Vadim Kimmelmann, Floris Roelofson, Galit W. Sassoon, Katrin Schulz & Matthijs Westera (eds.), Logic, Language, and Meaning: 18th Amsterdam Colloquium, Amsterdam, The Netherlands, December 19-21, 2011, Revised Selected Papers. Berlin: Springer. pp. 12-21.
    We investigate a basic probabilistic dynamic semantics for a fragment containing conditionals, probability operators, modals, and attitude verbs, with the aim of shedding light on the prospects for adding probabilistic structure to models of the conversational common ground.
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  9. Probabilistic Opinion Pooling.Franz Dietrich & Christian List - 2016 - In Alan Hajek & Christopher Hitchcock (eds.), Oxford Handbook of Philosophy and Probability. Oxford: Oxford University Press.
    Suppose several individuals (e.g., experts on a panel) each assign probabilities to some events. How can these individual probability assignments be aggregated into a single collective probability assignment? This article reviews several proposed solutions to this problem. We focus on three salient proposals: linear pooling (the weighted or unweighted linear averaging of probabilities), geometric pooling (the weighted or unweighted geometric averaging of probabilities), and multiplicative pooling (where probabilities are multiplied rather than averaged). We present axiomatic characterisations of each class of (...)
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  10. Probabilistic coherence and proper scoring rules.Joel Predd, Robert Seiringer, Elliott Lieb, Daniel Osherson, H. Vincent Poor & Sanjeev Kulkarni - 2009 - IEEE Transactions on Information Theory 55 (10):4786-4792.
    We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem recapitulates insights achieved by other investigators, and clarifi es the connection of coherence and proper scoring rules to Bregman divergence.
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  11. Are probabilism and special relativity incompatible?Nicholas Maxwell - 1985 - Philosophy of Science 52 (1):23-43.
    In this paper I expound an argument which seems to establish that probabilism and special relativity are incompatible. I examine the argument critically, and consider its implications for interpretative problems of quantum theory, and for theoretical physics as a whole.
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  12. Probabilistic causation.Christopher Hitchcock - 2008 - Stanford Encyclopedia of Philosophy.
    “Probabilistic Causation” designates a group of theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes change the probabilities of their effects. This article traces developments in probabilistic causation, including recent developments in causal modeling. A variety of issues within, and objections to, probabilistic theories of causation will also be discussed.
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  13. Cognitivist Probabilism.Paul D. Thorn - 2013 - In Vit Punochar & Petr Svarny (eds.), The Logica Yearbook 2012. College Publications. pp. 201-213.
    In this article, I introduce the term “cognitivism” as a name for the thesis that degrees of belief are equivalent to full beliefs about truth-valued propositions. The thesis (of cognitivism) that degrees of belief are equivalent to full beliefs is equivocal, inasmuch as different sorts of equivalence may be postulated between degrees of belief and full beliefs. The simplest sort of equivalence (and the sort of equivalence that I discuss here) identifies having a given degree of belief with having a (...)
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  14. Are probabilism and special relativity compatible?Nicholas Maxwell - 1988 - Philosophy of Science 55 (4):640-645.
    Are special relativity and probabilism compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal "now" (and is thus incompatible with special relativity), or amounts to a many world universe (which I have discussed, and rejected as too ad hoc to be taken seriously), or fails to have any one definite overall Minkowskian-type space-time structure (and thus differs drastically from special relativity as ordinarily understood). (...)
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  15. Radical probabilism and bayesian conditioning.Richard Bradley - 2005 - Philosophy of Science 72 (2):342-364.
    Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but (...)
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  16. Probabilistic measures of coherence: from adequacy constraints towards pluralism.Michael Schippers - 2014 - Synthese 191 (16):3821-3845.
    The debate on probabilistic measures of coherence flourishes for about 15 years now. Initiated by papers that have been published around the turn of the millennium, many different proposals have since then been put forward. This contribution is partly devoted to a reassessment of extant coherence measures. Focusing on a small number of reasonable adequacy constraints I show that (i) there can be no coherence measure that satisfies all constraints, and that (ii) subsets of these adequacy constraints motivate two different (...)
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  17. A probabilistic framework for analysing the compositionality of conceptual combinations.Peter Bruza, Kirsty Kitto, Brentyn Ramm & Laurianne Sitbon - 2015 - Journal of Mathematical Psychology 67:26-38.
    Conceptual combination performs a fundamental role in creating the broad range of compound phrases utilised in everyday language. This article provides a novel probabilistic framework for assessing whether the semantics of conceptual combinations are compositional, and so can be considered as a function of the semantics of the constituent concepts, or not. While the systematicity and productivity of language provide a strong argument in favor of assuming compositionality, this very assumption is still regularly questioned in both cognitive science and philosophy. (...)
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    Deductive, Probabilistic, and Inductive Dependence: An Axiomatic Study in Probability Semantics.Georg Dorn - 1997 - Verlag Peter Lang.
    This work is in two parts. The main aim of part 1 is a systematic examination of deductive, probabilistic, inductive and purely inductive dependence relations within the framework of Kolmogorov probability semantics. The main aim of part 2 is a systematic comparison of (in all) 20 different relations of probabilistic (in)dependence within the framework of Popper probability semantics (for Kolmogorov probability semantics does not allow such a comparison). Added to this comparison is an examination of (in all) 15 purely inductive (...)
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  19. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Judea Pearl - 1988 - Morgan Kaufmann.
    The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
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  20.  19
    Probabilistic Learning and Psychological Similarity.Nina Poth - 2023 - Entropy 25 (10).
    The notions of psychological similarity and probabilistic learning are key posits in cognitive, computational, and developmental psychology and in machine learning. However, their explanatory relationship is rarely made explicit within and across these research fields. This opinionated review critically evaluates how these notions can mutually inform each other within computational cognitive science. Using probabilistic models of concept learning as a case study, I argue that two notions of psychological similarity offer important normative constraints to guide modelers’ interpretations of representational primitives. (...)
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  21. Probabilistic opinion pooling generalised. Part two: The premise-based approach.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48 (4):787–814.
    How can different individuals' probability functions on a given sigma-algebra of events be aggregated into a collective probability function? Classic approaches to this problem often require 'event-wise independence': the collective probability for each event should depend only on the individuals' probabilities for that event. In practice, however, some events may be 'basic' and others 'derivative', so that it makes sense first to aggregate the probabilities for the former and then to let these constrain the probabilities for the latter. We formalize (...)
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  22.  32
    Probabilistic Logic and Probabilistic Networks. Haenni, R., Romeijn, J.-W., Wheeler, G. & Williamson, J. - unknown
    While in principle probabilistic logics might be applied to solve a range of problems, in practice they are rarely applied at present. This is perhaps because they seem disparate, complicated, and computationally intractable. However, we shall argue in this programmatic paper that several approaches to probabilistic logic into a simple unifying framework: logically complex evidence can be used to associate probability intervals or probabilities with sentences.
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  23.  8
    The Probabilistic Foundations of Rational Learning.Simon M. Huttegger - 2017 - Cambridge University Press.
    According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. (...)
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  24. Rational Probabilistic Incoherence.Michael Caie - 2013 - Philosophical Review 122 (4):527-575.
    Probabilism is the view that a rational agent's credences should always be probabilistically coherent. It has been argued that Probabilism follows, given the assumption that an epistemically rational agent ought to try to have credences that represent the world as accurately as possible. The key claim in this argument is that the goal of representing the world as accurately as possible is best served by having credences that are probabilistically coherent. This essay shows that this claim is false. (...)
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  25. Probabilistic semantics for epistemic modals: Normality assumptions, conditional epistemic spaces and the strength of must and might.Guillermo Del Pinal - 2021 - Linguistics and Philosophy 45 (4):985-1026.
    The epistemic modal auxiliaries must and might are vehicles for expressing the force with which a proposition follows from some body of evidence or information. Standard approaches model these operators using quantificational modal logic, but probabilistic approaches are becoming increasingly influential. According to a traditional view, must is a maximally strong epistemic operator and might is a bare possibility one. A competing account—popular amongst proponents of a probabilisitic turn—says that, given a body of evidence, must \ entails that \\) is (...)
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  26. Probabilistic Opinion Pooling Generalized. Part One: General Agendas.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48 (4):747–786.
    How can different individuals' probability assignments to some events be aggregated into a collective probability assignment? Classic results on this problem assume that the set of relevant events -- the agenda -- is a sigma-algebra and is thus closed under disjunction (union) and conjunction (intersection). We drop this demanding assumption and explore probabilistic opinion pooling on general agendas. One might be interested in the probability of rain and that of an interest-rate increase, but not in the probability of rain or (...)
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  27. Probabilistic theories of causality.Jon Williamson - 2009 - In Helen Beebee, Peter Menzies & Christopher Hitchcock (eds.), The Oxford Handbook of Causation. Oxford University Press. pp. 185--212.
    This chapter provides an overview of a range of probabilistic theories of causality, including those of Reichenbach, Good and Suppes, and the contemporary causal net approach. It discusses two key problems for probabilistic accounts: counterexamples to these theories and their failure to account for the relationship between causality and mechanisms. It is argued that to overcome the problems, an epistemic theory of causality is required.
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  28.  52
    A Probabilistic Computational Model of Cross-Situational Word Learning.Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson - 2010 - Cognitive Science 34 (6):1017-1063.
    Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement (...)
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  29.  65
    Probabilistic metaphysics.Patrick Suppes - 1984 - New York, NY, USA: Blackwell.
  30.  22
    Probabilistic Knowledge as Objective Knowledge in Quantum Mechanics: Potential Powers Instead of Actual Properties.Christian de Ronde - unknown
    In classical physics, probabilistic or statistical knowledge has been always related to ignorance or inaccurate subjective knowledge about an actual state of affairs. This idea has been extended to quantum mechanics through a completely incoherent interpretation of the Fermi-Dirac and Bose-Einstein statistics in terms of "strange" quantum particles. This interpretation, naturalized through a widespread "way of speaking" in the physics community, contradicts Born's physical account of Ψ as a "probability wave" which provides statistical information about outcomes that, in fact, cannot (...)
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  31. Probabilistic reasoning in clinical medicine: Problems and opportunities.David M. Eddy - 1982 - In Daniel Kahneman, Paul Slovic & Amos Tversky (eds.), Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press. pp. 249--267.
  32.  21
    Probabilistic inferences from conjoined to iterated conditionals.Giuseppe Sanfilippo, Niki Pfeifer, D. E. Over & A. Gilio - 2018 - International Journal of Approximate Reasoning 93:103-118.
    There is wide support in logic, philosophy, and psychology for the hypothesis that the probability of the indicative conditional of natural language, P(if A then B), is the conditional probability of B given A, P(B|A). We identify a conditional which is such that P(if A then B)=P(B|A) with de Finetti's conditional event, B|A. An objection to making this identification in the past was that it appeared unclear how to form compounds and iterations of conditional events. In this paper, we illustrate (...)
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  33. Probabilistic causation and the explanatory role of natural selection.Pablo Razeto-Barry & Ramiro Frick - 2011 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 42 (3):344-355.
    The explanatory role of natural selection is one of the long-term debates in evolutionary biology. Nevertheless, the consensus has been slippery because conceptual confusions and the absence of a unified, formal causal model that integrates different explanatory scopes of natural selection. In this study we attempt to examine two questions: (i) What can the theory of natural selection explain? and (ii) Is there a causal or explanatory model that integrates all natural selection explananda? For the first question, we argue that (...)
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  34. A Probabilistic Theory of Causality.P. Suppes - 1973 - British Journal for the Philosophy of Science 24 (4):409-410.
     
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  35. Probabilistic theories of reasoning need pragmatics too: Modulating relevance in uncertain conditionals.A. J. B. Fugard, Niki Pfeifer & B. Mayerhofer - 2011 - Journal of Pragmatics 43:2034–2042.
    According to probabilistic theories of reasoning in psychology, people's degree of belief in an indicative conditional `if A, then B' is given by the conditional probability, P(B|A). The role of language pragmatics is relatively unexplored in the new probabilistic paradigm. We investigated how consequent relevance a ects participants' degrees of belief in conditionals about a randomly chosen card. The set of events referred to by the consequent was either a strict superset or a strict subset of the set of events (...)
     
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  36. Are Probabilism and Special Relativity Compatible?Nicholas Maxwell - 1988 - Philosophy of Science 55 (4):640-645.
    Are probabilism and special relativity compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal “now”, or amounts to a many world universe, or fails to have any one definite overall Minkowskian-type space-time structure. Probabilism and special relativity appear to be incompatible after all. What is at issue is not whether “the flow of time” can be reconciled with special relativity, but rather whether (...)
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  37. Does probabilism solve the great quantum mystery?Nicholas Maxwell - 2010 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 19 (3):321-336.
    I put forward a micro realistic, probabilistic version of quantum theory, which specifies the precise nature of quantum entities thus solving the quantum wave/particle dilemma, and which both reproduces the empirical success of orthodox quantum theory, and yields predictions that differ from orthodox quantum theory for as yet unperformed experiments.
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  38. Probabilistic Proofs, Lottery Propositions, and Mathematical Knowledge.Yacin Hamami - 2021 - Philosophical Quarterly 72 (1):77-89.
    In mathematics, any form of probabilistic proof obtained through the application of a probabilistic method is not considered as a legitimate way of gaining mathematical knowledge. In a series of papers, Don Fallis has defended the thesis that there are no epistemic reasons justifying mathematicians’ rejection of probabilistic proofs. This paper identifies such an epistemic reason. More specifically, it is argued here that if one adopts a conception of mathematical knowledge in which an epistemic subject can know a mathematical proposition (...)
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    Probabilistic logic under coherence, model-theoretic probabilistic logic, and default reasoning in System P.Veronica Biazzo, Angelo Gilio, Thomas Lukasiewicz & Giuseppe Sanfilippo - 2002 - Journal of Applied Non-Classical Logics 12 (2):189-213.
    We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail, we explore how probabilistic reasoning under coherence is related to model- theoretic probabilistic reasoning and to default reasoning in System . In particular, we show that the notions of g-coherence and of g-coherent entailment can be expressed by combining notions in model-theoretic probabilistic logic with concepts from default reasoning. Moreover, we show that probabilistic reasoning under coherence is a generalization of default reasoning in System (...)
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  40. A Probabilistic Defense of Proper De Jure Objections to Theism.Brian C. Barnett - 2019
    A common view among nontheists combines the de jure objection that theism is epistemically unacceptable with agnosticism about the de facto objection that theism is false. Following Plantinga, we can call this a “proper” de jure objection—a de jure objection that does not depend on any de facto objection. In his Warranted Christian Belief, Plantinga has produced a general argument against all proper de jure objections. Here I first show that this argument is logically fallacious (it makes subtle probabilistic fallacies (...)
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  41.  32
    Reconstructing Probabilistic Realism: Re-enacting Syntactical Structures.Majid Davoody Beni - 2020 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 51 (2):293-313.
    Probabilistic realism and syntactical positivism were two among outdated theories that Feigl criticised on account of their semantical poverty. In this paper, I argue that a refined version of probabilistic realism, which relies on what Feigl specified as the pragmatic description of the symbolic behaviour of scientists’ estimations and foresight, is defendable. This version of statistical realism does not need to make the plausibility of realist thesis dependent on the conventional acceptance of a constructed semantic metalanguage. I shall rely on (...)
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  42.  45
    Non-probabilistic Causation without Necessitation.Daniel Von Wachter - manuscript
    This article introduces the notion of the directedness of a process, which underlies event causation as well as the persistence of things. Using this notion it investigates what happens in typical cases of active event causation. Causes never necessitate their effects because even non- probabilistic causes can be counteracted.
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    A Probabilistic Model of Melody Perception.David Temperley - 2008 - Cognitive Science 32 (2):418-444.
    This study presents a probabilistic model of melody perception, which infers the key of a melody and also judges the probability of the melody itself. The model uses Bayesian reasoning: For any “surface” pattern and underlying “structure,” we can infer the structure maximizing P(structure|surface) based on knowledge of P(surface, structure). The probability of the surface can then be calculated as ∑ P(surface, structure), summed over all structures. In this case, the surface is a pattern of notes; the structure is a (...)
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  44. PROBABILISTIC APPROACH TO EPISTEMIC MODALS IN THE FRAMEWORK OF DYNAMIC SEMANTICS.Milana Kostic - 2015 - Hybris, Revista de Filosofí­A (30):016-032.
    PROBABILISTIC APPROACH TO EPISTEMIC MODALS IN THE FRAMEWORK OF DYNAMIC SEMANTICS In dynamic semantics meaning of a statement is not equated with its truth conditions but with its context change potential. It has also been claimed that dynamic framework can automatically account for certain paradoxes that involve epistemic modals, such as the following one: it seems odd and incoherent to claim: (1) “It is raining and it might not rain”, whereas claiming (2) “It might not rain and it is raining” (...)
     
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  45. Probabilistic causality and the question of transitivity.Ellery Eells & Elliott Sober - 1983 - Philosophy of Science 50 (1):35-57.
    After clarifying the probabilistic conception of causality suggested by Good (1961-2), Suppes (1970), Cartwright (1979), and Skyrms (1980), we prove a sufficient condition for transitivity of causal chains. The bearing of these considerations on the units of selection problem in evolutionary theory and on the Newcomb paradox in decision theory is then discussed.
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  46.  42
    Probabilistic stability, agm revision operators and maximum entropy.Krzysztof Mierzewski - 2020 - Review of Symbolic Logic:1-38.
    Several authors have investigated the question of whether canonical logic-based accounts of belief revision, and especially the theory of AGM revision operators, are compatible with the dynamics of Bayesian conditioning. Here we show that Leitgeb's stability rule for acceptance, which has been offered as a possible solution to the Lottery paradox, allows to bridge AGM revision and Bayesian update: using the stability rule, we prove that AGM revision operators emerge from Bayesian conditioning by an application of the principle of maximum (...)
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    Probabilistic models of language processing and acquisition.Nick Chater & Christopher D. Manning - 2006 - Trends in Cognitive Sciences 10 (7):335–344.
    Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online (...)
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  48. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.J. Pearl, F. Bacchus, P. Spirtes, C. Glymour & R. Scheines - 1988 - Synthese 104 (1):161-176.
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  49. Probabilistic models of cognition: Conceptual foundations.Nick Chater & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):287-291.
    Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore (...)
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    Probabilistic Explanations.James H. Fetzer - 1982 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:194-207.
    The purpose of this paper is to provide a systematic defense of the single-case propensity account of probabilistic explanation from the criticisms advanced by Hanna and by Humphreys and to offer a critical appraisal of the aleatory conception advanced by Humphreys and of the deductive-nomological-probabilistic approach Railton has proposed. The principal conclusion supported by this analysis is that the Requirements of Maximal Specificity and of Strict Maximal Specificity afford the foundation for completely objective explanations of probabilistic explananda, so long as (...)
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