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Ambiguity aversion, modern Bayesianism and small worlds [version 1; peer review: 2 approved] [PDF]
Open Research Europe, 2021 The central question of this paper is whether a rational agent under uncertainty can exhibit ambiguity aversion (AA). The answer to this question depends on the way the agent forms her probabilistic beliefs: classical Bayesianism (CB) vs modern ...
Nikitas Pittis +4 more
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A Snapshot of Bayesianism [PDF]
EntropyStudents are told in basic probability classes that there are two main “schools” of statistics, the frequentist and the Bayesian, and that those different views of how to approach statistical inference problems arise from two different views of the ...
Mark A. Gannon
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Empirical Bayes Methods, Evidentialism, and the Inferential Roles They Play [PDF]
EntropyEmpirical Bayes-based Methods (EBM) is an increasingly popular form of Objective Bayesianism (OB). It is identified in particular with the statistician Bradley Efron.
Samidha Shetty +2 more
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Authors' reply to 'Multiple comparisons controversies are about context and costs, not frequentism versus Bayesianism'. [PDF]
Eur J Epidemiol, 2020 A
Sjölander A, Vansteelandt S.
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Entropy of the Canonical Occupancy (Macro) State in the Quantum Measurement Theory [PDF]
EntropyThe paper analyzes the probability distribution of the occupancy numbers and the entropy of a system at the equilibrium composed by an arbitrary number of non-interacting bosons.
Arnaldo Spalvieri
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Probabilistic Alternatives to Bayesianism: The Case of Explanationism [PDF]
Frontiers in Psychology, 2015 There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative ...
Igor eDouven, Jonah N. Schupbach
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A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning [PDF]
Entropy, 2022 Philosophers frequently define knowledge as justified, true belief. We built a mathematical framework that makes it possible to define learning (increasing number of true beliefs) and knowledge of an agent in precise ways, by phrasing belief in terms of ...
Ola Hössjer +2 more
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Dynamics, 2023 In the cognitive and neural sciences, Bayesianism refers to a collection of concepts and methods stemming from various implementations of Bayes’ theorem, which is a formal way to calculate the conditional probability of a hypothesis being true based on ...
Luis H. Favela, Mary Jean Amon
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Commentary: The Predictive Processing Paradigm Has Roots in Kant [PDF]
Frontiers in Systems Neuroscience, 2018 Majid D. Beni
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The measurement problem in Quantum Mechanics: Convivial Solipsism [PDF]
EPJ Web of Conferences, 2020 The problem of measurement is often considered an inconsistency inside the quantum formalism. Many attempts to solve (or to dissolve) it have been made since the inception of quantum mechanics.
Zwirn Hervé
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