Results 21 to 30 of about 5,203 (262)
The American Statistician, 2019 Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework.
Pierre Baldi, Babak Shahbaba
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Free Will: A consensus gentium Argument [PDF]
Organon FThis argument for free will is a probabilistic one based upon two conjectures: first, that of consensus; namely, that a large majority of people believe that they and others have free will and second, that a priori proofs against the existence of free ...
William Hunt
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Machine Learning: Science and Technology, 2023 Abstract In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian renormalization. The main insight of Bayesian renormalization is that the Fisher metric defines a correlation length that plays the role of an emergent renormalization group (
David S Berman +2 more
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The Problem of New Evidence: P-Hacking and Pre-Analysis Plans
Diametros, 2020 We provide a novel articulation of the epistemic peril of p-hacking using three resources from philosophy: predictivism, Bayesian confirmation theory, and model selection theory.
Zoe Hitzig, Jacob Stegenga
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The heuristic conception of inference to the best explanation [PDF]
, 2017 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.
Dellsén, Finnur
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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
Jürgen Landes, Jon Williamson
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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, Jürgen Landes
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DELIBERATION, JUDGEMENT AND THE NATURE OF EVIDENCE [PDF]
, 2014 A normative Bayesian theory of deliberation and judgement requires a procedure for merging the evidence of a collection of agents. In order to provide such a procedure, one needs to ask what the evidence is that grounds Bayesian probabilities.
Dawid +13 more
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Bayesianism for Non-ideal Agents [PDF]
, 2020 Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience: the assumption that an agent’s degrees of belief must be probabilistically ...
Bjerring, Jens Christian +1 more
core
, 2009 By representing the range of fair betting odds according to a pair of confidence set estimators, dual probability measures on parameter space called frequentist posteriors secure the coherence of subjective inference without any prior distribution.
Datta G. S. +16 more
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