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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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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 ...
Jonah N Schupbach +2 more
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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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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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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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Justifying Objective Bayesianism on Predicate Languages
Entropy, 2015 Objective Bayesianism says that the strengths of one’s beliefs ought to be probabilities, calibrated to physical probabilities insofar as one has evidence of them, and otherwise sufficiently equivocal. These norms of belief are often explicated using the
Jon Williamson +2 more
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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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Objective Bayesianism and the Maximum Entropy Principle
Entropy, 2013 Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities; they should be calibrated to our evidence of physical probabilities; and they should otherwise equivocate sufficiently between the basic ...
Jon Williamson +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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