Results for 'Bayesian networks'

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  1. Cultural Evolution in Vietnam’s Early 20th Century: A Bayesian Networks Analysis of 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 - manuscript
    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 (...)
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  2.  65
    Coherence, Belief Expansion and Bayesian Networks.Luc Bovens & Stephan Hartmann - 2000 - In C. Baral (ed.), Proceedings of the 8th International Workshop on Non-Monotonic Reasoning, NMR'2000.
    We construct a probabilistic coherence measure for information sets which determines a partial coherence ordering. This measure is applied in constructing a criterion for expanding our beliefs in the face of new information. A number of idealizations are being made which can be relaxed by an appeal to Bayesian Networks.
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  3. Bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'.Quan-Hoang Vuong & Viet-Phuong La - 2019 - Open Science Framework 2019:01-47.
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  4.  42
    A Probabilistic Analysis of Cross-Examination Using Bayesian Networks.Marcello Di Bello - 2021 - Philosophical Issues 31 (1):41-65.
    The legal scholar Henry Wigmore asserted that cross-examination is ‘the greatest legal engine ever invented for the discovery of truth.’ Was Wigmore right? Instead of addressing this question upfront, this paper offers a conceptual ground clearing. It is difficult to say whether Wigmore was right or wrong without becoming clear about what we mean by cross-examination; how it operates at trial; what it is intended to accomplish. Despite the growing importance of legal epistemology, there is virtually no philosophical work that (...)
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  5.  28
    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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  6. Improving Bayesian Statistics Understanding in the Age of Big Data with the Bayesvl R Package.Quan-Hoang Vuong, Viet-Phuong La, Minh-Hoang Nguyen, Manh-Toan Ho, Manh-Tung Ho & Peter Mantello - 2020 - Software Impacts 4 (1):100016.
    The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines (...)
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  7. Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity.Patrick Grim, Frank Seidl, Calum McNamara, Hinton Rago, Isabell Astor, Caroline Diaso & Peter Ryner - forthcoming - Philosophy of Science.
    We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to (...)
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  8. Bayesian Models and Simulations in Cognitive Science.Giuseppe Boccignone & Roberto Cordeschi - 2007 - Workshop Models and Simulations 2, Tillburg, NL.
    Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.
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  9.  42
    Bayesian Test of Significance for Conditional Independence: The Multinomial Model.Julio Michael Stern, Pablo de Morais Andrade & Carlos Alberto de Braganca Pereira - 2014 - Entropy 16:1376-1395.
    Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian network models, conditional independence tests are especially important for the task of learning the probabilistic graphical model structure from data. In this paper, we propose the full Bayesian significance test for tests of conditional independence for discrete datasets. The (...)
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  10.  15
    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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  11. Knowledge Bases and Neural Network Synthesis.Todd R. Davies - 1991 - In Hozumi Tanaka (ed.), Artificial Intelligence in the Pacific Rim: Proceedings of the Pacific Rim International Conference on Artificial Intelligence. IOS Press. pp. 717-722.
    We describe and try to motivate our project to build systems using both a knowledge based and a neural network approach. These two approaches are used at different stages in the solution of a problem, instead of using knowledge bases exclusively on some problems, and neural nets exclusively on others. The knowledge base (KB) is defined first in a declarative, symbolic language that is easy to use. It is then compiled into an efficient neural network (NN) representation, run, and the (...)
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  12. 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 theory (...)
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  13. On How Religions Could Accidentally Incite Lies and Violence: Folktales as a Cultural Transmitter.Quan-Hoang Vuong, Ho Manh Tung, Nguyen To Hong Kong, La Viet Phuong, Vuong Thu Trang, Vu Thi Hanh, Nguyen Minh Hoang & Manh-Toan Ho - manuscript
    This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the final outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or (...)
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  14. A Model of Jury Decisions Where All Jurors Have the Same Evidence.Franz Dietrich & Christian List - 2004 - Synthese 142 (2):175 - 202.
    Under the independence and competence assumptions of Condorcet’s classical jury model, the probability of a correct majority decision converges to certainty as the jury size increases, a seemingly unrealistic result. Using Bayesian networks, we argue that the model’s independence assumption requires that the state of the world (guilty or not guilty) is the latest common cause of all jurors’ votes. But often – arguably in all courtroom cases and in many expert panels – the latest such common cause (...)
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  15. The Structure of Epistemic Probabilities.Nevin Climenhaga - 2020 - Philosophical Studies 177 (11):3213-3242.
    The epistemic probability of A given B is the degree to which B evidentially supports A, or makes A plausible. This paper is a first step in answering the question of what determines the values of epistemic probabilities. I break this question into two parts: the structural question and the substantive question. Just as an object’s weight is determined by its mass and gravitational acceleration, some probabilities are determined by other, more basic ones. The structural question asks what probabilities are (...)
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  16.  84
    A Response to Prelec.Luc Bovens - 2013 - In Adam Oliver (ed.), Essays in Behavioural Public Policy. Cambridge: Cambridge University Press. pp. 228-33.
    At the heart of Drazen Prelec’s chapter is the distinction between outcome utility and diagnostic utility. There is a particular distinction in the literature on causal networks (Pearl 2000), namely the distinction between observing and intervening, that maps onto Prelec’s distinction between diagnostic and outcome utility. I will explore the connection between both frameworks.
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  17. Aggregating Causal Judgments.Richard Bradley, Franz Dietrich & Christian List - 2014 - Philosophy of Science 81 (4):491-515.
    Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, we introduce the problem of causal-network aggregation. (...)
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  18. Robustness and Independent Evidence.Jacob Stegenga & Tarun Menon - 2017 - Philosophy of Science 84 (3):414-435.
    Robustness arguments hold that hypotheses are more likely to be true when they are confirmed by diverse kinds of evidence. Robustness arguments require the confirming evidence to be independent. We identify two kinds of independence appealed to in robustness arguments: ontic independence —when the multiple lines of evidence depend on different materials, assumptions, or theories—and probabilistic independence. Many assume that OI is sufficient for a robustness argument to be warranted. However, we argue that, as typically construed, OI is not a (...)
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  19.  84
    Bản hòa tấu dữ liệu xã hội.Vuong Quan-Hoang, La Viet-Phuong, Tran Trung, Nguyen Minh-Hoang & Ho Manh-Toan - 2021 - Hà Nội: Nhà xuất bản khoa học xã hội.
    Chương trình bayesvl được thiết kế với định hướng sư phạm, hỗ trợ các nhà nghiên cứu ngành khoa học xã hội và nhân văn sử dụng mô hình lưới Bayesian, mô phỏng MCMC, hình ảnh hóa các thông số kĩ thuật và phân tích dữ liệu xã hội. bayesvl được phát hành chính thức trên hệ thống thư viện chuẩn của R là Comprehensive R Archive Network (CRAN) vào tháng 5 năm 2019.
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  20.  19
    Rapid Initiative Assessment for Counter-IED Investment.Charles Twardy, Ed Wright, Tod Levitt, Kathryn Laskey & Kellen Leister - 2009 - In Proceedings of the Seventh Bayesian Applications Modeling Workshop.
    There is a need to rapidly assess the impact of new technology initiatives on the Counter Improvised Explosive Device battle in Iraq and Afghanistan. The immediate challenge is the need for rapid decisions, and a lack of engineering test data to support the assessment. The rapid assessment methodology exploits available information to build a probabilistic model that provides an explicit executable representation of the initiative’s likely impact. The model is used to provide a consistent, explicit, explanation to decision makers on (...)
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  21. Antireductionist Interventionism.Reuben Stern & Benjamin Eva - forthcoming - British Journal for the Philosophy of Science.
    Kim’s causal exclusion argument purports to demonstrate that the non-reductive physicalist must treat mental properties (and macro-level properties in general) as causally inert. A number of authors have attempted to resist Kim’s conclusion by utilizing the conceptual resources of Woodward’s (2005) interventionist conception of causation. The viability of these responses has been challenged by Gebharter (2017a), who argues that the causal exclusion argument is vindicated by the theory of causal Bayesian networks (CBNs). Since the interventionist conception of causation (...)
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  22. Understanding the Interplay of Lies, Violence, and Religious Values in Folktales.Quan-Hoang Vuong, Viet-Phuong La & Hong-Kong T. Nguyen - manuscript
    This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (...)
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  23. Where’s the Biff?Toby Handfield, Charles R. Twardy, Kevin B. Korb & Graham Oppy - 2008 - Erkenntnis 68 (2):149-68.
    This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late preëmption and (...)
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  24. Wisdom of the Crowds Vs. Groupthink: Learning in Groups and in Isolation.Conor Mayo-Wilson, Kevin Zollman & David Danks - 2013 - International Journal of Game Theory 42 (3):695-723.
    We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the appropriateness of various, common boundedly-rational strategies depends crucially upon the social context (...)
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  25. Reputation Risks, Value of Losses and Financial Sustainability of Commercial Banks.Natalia Kunitsyna, Igor Britchenko & Igor Kunitsyn - 2018 - Entrepreneurship and Sustainability Issues 5 (4):943-955.
    Currently, under the conditions of permanent financial risks that hamper the sustainable economic growth in the financial sector, the development of evaluation and risk management methods both regulated by Basel II and III and others seem to be of special importance. The reputation risk is one of significant risks affecting reliability and credibility of commercial banks. The importance of reputation risk management and the quality of their assessment remain relevant as the probability of decrease in or loss of business reputation (...)
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  26. Should Causal Models Always Be Markovian? The Case of Multi-Causal Forks in Medicine.Donald Gillies & Aidan Sudbury - 2013 - European Journal for Philosophy of Science 3 (3):275-308.
    The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position by considering the multi-causal (...)
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  27.  30
    Causal Feature Learning for Utility-Maximizing Agents.David Kinney & David Watson - 2020 - In International Conference on Probabilistic Graphical Models. pp. 257–268.
    Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, (...)
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  28. A Scientist’s Walk Through Hanoi Old Quarter Houses.Bui Dieu Quynh - manuscript
    Hanoi – the capital city of Vietnam and the land of thousand years of civilization – invokes among both locals and tourists the image of the ‘Sword Lake’ with its ancient ‘Turtle Tower’ and the charming Old Quarter with its preserved old houses lying along small commercial alleys. The houses in the Old Quarter were constructed over a century ago which feature tube houses with inclined tile roofs and a blend of French architecture create the infusions of history and memory. (...)
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  29.  71
    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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  30. Explanation, Confirmation, and Hempel's Paradox.William Roche - 2017 - In Kevin McCain & Ted Poston (eds.), Best explanations: New essays on inference to the best explanation. Oxford: Oxford University Press. pp. 219-241.
    Hempel’s Converse Consequence Condition (CCC), Entailment Condition (EC), and Special Consequence Condition (SCC) have some prima facie plausibility when taken individually. Hempel, though, shows that they have no plausibility when taken together, for together they entail that E confirms H for any propositions E and H. This is “Hempel’s paradox”. It turns out that Hempel’s argument would fail if one or more of CCC, EC, and SCC were modified in terms of explanation. This opens up the possibility that Hempel’s paradox (...)
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  31. Is Religion a Necessary Condition for the Emergence of Knowledge? Some Explanatory Hypotheses.Viorel Rotilă - 2019 - Postmodern Openings 10 (3):202-228.
    By using the general investigation framework offered by the cognitive science of religion (CSR), I analyse religion as a necessary condition for the evolutionary path of knowledge. The main argument is the "paradox of the birth of knowledge": in order to get to the meaning of the part, a sense context is needed; but a sense of the whole presupposes the sense (meaning) of the parts. Religion proposes solutions to escape this paradox, based on the imagination of sense (meaning) contexts, (...)
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  32.  76
    Consensus-Based Data Management Within Fog Computing For the Internet of Things.Al-Doghman Firas Qais Mohammed Saleh - 2019 - Dissertation, University of Technology Sydney
    The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. This results (...)
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  33. The Bayesian and the Dogmatist.Brian Weatherson - 2007 - Proceedings of the Aristotelian Society 107 (1pt2):169-185.
    Dogmatism is sometimes thought to be incompatible with Bayesian models of rational learning. I show that the best model for updating imprecise credences is compatible with dogmatism.
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  34. Bayesian Realism and Structural Representation.Alex Kiefer & Jakob Hohwy - forthcoming - Behavioral and Brain Sciences.
    We challenge Bruineberg et al's view that Markov blankets are illicitly reified when used to describe organismic boundaries. We do this both on general methodological grounds, where we appeal to a form of structural realism derived from Bayesian cognitive science to dissolve the problem, and by rebutting specific arguments in the target article.
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  35. Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).
    It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon (...)
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  36.  97
    Bayesian Group Belief.Franz Dietrich - 2010 - Social Choice and Welfare 35 (4):595-626.
    If a group is modelled as a single Bayesian agent, what should its beliefs be? I propose an axiomatic model that connects group beliefs to beliefs of group members, who are themselves modelled as Bayesian agents, possibly with different priors and different information. Group beliefs are proven to take a simple multiplicative form if people’s information is independent, and a more complex form if information overlaps arbitrarily. This shows that group beliefs can incorporate all information spread over the (...)
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  37. Bayesian Decision Theory and Stochastic Independence.Philippe Mongin - 2020 - Philosophy of Science 87 (1):152-178.
    As stochastic independence is essential to the mathematical development of probability theory, it seems that any foundational work on probability should be able to account for this property. Bayesian decision theory appears to be wanting in this respect. Savage’s postulates on preferences under uncertainty entail a subjective expected utility representation, and this asserts only the existence and uniqueness of a subjective probability measure, regardless of its properties. What is missing is a preference condition corresponding to stochastic independence. To fill (...)
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  38. 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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  39. Bayesian Orgulity.Gordon Belot - 2013 - Philosophy of Science 80 (4):483-503.
    A piece of folklore enjoys some currency among philosophical Bayesians, according to which Bayesian agents that, intuitively speaking, spread their credence over the entire space of available hypotheses are certain to converge to the truth. The goals of the present discussion are to show that kernel of truth in this folklore is in some ways fairly small and to argue that Bayesian convergence-to-the-truth results are a liability for Bayesianism as an account of rationality, since they render a certain (...)
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  40. A Bayesian Explanation of the Irrationality of Sexist and Racist Beliefs Involving Generic Content.Paul Silva - 2020 - Synthese 197 (6):2465-2487.
    Various sexist and racist beliefs ascribe certain negative qualities to people of a given sex or race. Epistemic allies are people who think that in normal circumstances rationality requires the rejection of such sexist and racist beliefs upon learning of many counter-instances, i.e. members of these groups who lack the target negative quality. Accordingly, epistemic allies think that those who give up their sexist or racist beliefs in such circumstances are rationally responding to their evidence, while those who do not (...)
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  41. Bayesian Decision Theory and Stochastic Independence.Philippe Mongin - 2017 - TARK 2017.
    Stochastic independence has a complex status in probability theory. It is not part of the definition of a probability measure, but it is nonetheless an essential property for the mathematical development of this theory. Bayesian decision theorists such as Savage can be criticized for being silent about stochastic independence. From their current preference axioms, they can derive no more than the definitional properties of a probability measure. In a new framework of twofold uncertainty, we introduce preference axioms that entail (...)
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  42. 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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  43. Bayesian Epistemology.Alan Hájek & Stephan Hartmann - 2010 - In DancyJ (ed.), A Companion to Epistemology. Blackwell.
    Bayesianism is our leading theory of uncertainty. Epistemology is defined as the theory of knowledge. So “Bayesian Epistemology” may sound like an oxymoron. Bayesianism, after all, studies the properties and dynamics of degrees of belief, understood to be probabilities. Traditional epistemology, on the other hand, places the singularly non-probabilistic notion of knowledge at centre stage, and to the extent that it traffics in belief, that notion does not come in degrees. So how can there be a Bayesian epistemology?
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  44. A Bayesian Analysis of Debunking Arguments in Ethics.Shang Long Yeo - forthcoming - Philosophical Studies:1-20.
    Debunking arguments in ethics contend that our moral beliefs have dubious evolutionary, cultural, or psychological origins – hence concluding that we should doubt such beliefs. Debates about debunking are often couched in coarse-grained terms – about whether our moral beliefs are justified or not, for instance. In this paper, I propose a more detailed Bayesian analysis of debunking arguments, which proceeds in the fine-grained framework of rational confidence. Such analysis promises several payoffs: it highlights how debunking arguments don’t affect (...)
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  45.  28
    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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  46. Bayesian Confirmation: A Means with No End.Peter Brössel & Franz Huber - 2015 - British Journal for the Philosophy of Science 66 (4):737-749.
    Any theory of confirmation must answer the following question: what is the purpose of its conception of confirmation for scientific inquiry? In this article, we argue that no Bayesian conception of confirmation can be used for its primary intended purpose, which we take to be making a claim about how worthy of belief various hypotheses are. Then we consider a different use to which Bayesian confirmation might be put, namely, determining the epistemic value of experimental outcomes, and thus (...)
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  47. Network Representation and Complex Systems.Charles Rathkopf - 2018 - Synthese (1).
    In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties (...)
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  48. Bayesian Coherentism.Lisa Cassell - 2021 - Synthese 198 (10):9563-9590.
    This paper considers a problem for Bayesian epistemology and proposes a solution to it. On the traditional Bayesian framework, an agent updates her beliefs by Bayesian conditioning, a rule that tells her how to revise her beliefs whenever she gets evidence that she holds with certainty. In order to extend the framework to a wider range of cases, Jeffrey (1965) proposed a more liberal version of this rule that has Bayesian conditioning as a special case. Jeffrey (...)
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  49. Bayesian Models, Delusional Beliefs, and Epistemic Possibilities.Matthew Parrott - 2014 - British Journal for the Philosophy of Science (1):axu036.
    The Capgras delusion is a condition in which a person believes that an imposter has replaced some close friend or relative. Recent theorists have appealed to Bayesianism to help explain both why a subject with the Capgras delusion adopts this delusional belief and why it persists despite counter-evidence. The Bayesian approach is useful for addressing these questions; however, the main proposal of this essay is that Capgras subjects also have a delusional conception of epistemic possibility, more specifically, they think (...)
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  50. For Bayesians, Rational Modesty Requires Imprecision.Brian Weatherson - 2015 - Ergo: An Open Access Journal of Philosophy 2.
    Gordon Belot has recently developed a novel argument against Bayesianism. He shows that there is an interesting class of problems that, intuitively, no rational belief forming method is likely to get right. But a Bayesian agent’s credence, before the problem starts, that she will get the problem right has to be 1. This is an implausible kind of immodesty on the part of Bayesians. My aim is to show that while this is a good argument against traditional, precise Bayesians, (...)
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