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  1. The status of the principle of maximum entropy.Abner Shimony - 1985 - Synthese 63 (1):35 - 53.
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  • Objective Bayesianism, Bayesian conditionalisation and voluntarism.Jon Williamson - 2011 - Synthese 178 (1):67-85.
    Objective Bayesianism has been criticised on the grounds that objective Bayesian updating, which on a finite outcome space appeals to the maximum entropy principle, differs from Bayesian conditionalisation. The main task of this paper is to show that this objection backfires: the difference between the two forms of updating reflects negatively on Bayesian conditionalisation rather than on objective Bayesian updating. The paper also reviews some existing criticisms and justifications of conditionalisation, arguing in particular that the diachronic Dutch book justification fails (...)
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  • Updating Probability: Tracking Statistics as Criterion.Bas C. van Fraassen & Joseph Y. Halpern - 2016 - British Journal for the Philosophy of Science:axv027.
    ABSTRACT For changing opinion, represented by an assignment of probabilities to propositions, the criterion proposed is motivated by the requirement that the assignment should have, and maintain, the possibility of matching in some appropriate sense statistical proportions in a population. This ‘tracking’ criterion implies limitations on policies for updating in response to a wide range of types of new input. Satisfying the criterion is shown equivalent to the principle that the prior must be a convex combination of the possible posteriors. (...)
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  • The Brandeis Dice Problem and Statistical Mechanics.Steven J. van Enk - 2014 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 48 (1):1-6.
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  • The constraint rule of the maximum entropy principle.Jos Uffink - 1996 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 27 (1):47-79.
    The principle of maximum entropy is a method for assigning values to probability distributions on the basis of partial information. In usual formulations of this and related methods of inference one assumes that this partial information takes the form of a constraint on allowed probability distributions. In practical applications, however, the information consists of empirical data. A constraint rule is then employed to construct constraints on probability distributions out of these data. Usually one adopts the rule that equates the expectation (...)
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  • Updating, supposing, and maxent.Brian Skyrms - 1987 - Theory and Decision 22 (3):225-246.
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  • Contemporary Approaches to Statistical Mechanical Probabilities: A Critical Commentary - Part I: The Indifference Approach.Christopher J. G. Meacham - 2010 - Philosophy Compass 5 (12):1116-1126.
    This pair of articles provides a critical commentary on contemporary approaches to statistical mechanical probabilities. These articles focus on the two ways of understanding these probabilities that have received the most attention in the recent literature: the epistemic indifference approach, and the Lewis-style regularity approach. These articles describe these approaches, highlight the main points of contention, and make some attempts to advance the discussion. The first of these articles provides a brief sketch of statistical mechanics, and discusses the indifference approach (...)
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  • Artificial Intelligence: The Basics.C. Maria Keet - 2012 - International Studies in the Philosophy of Science 26 (3):351-354.
    International Studies in the Philosophy of Science, Volume 26, Issue 3, Page 351-354, September 2012.
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  • Merging of opinions and probability kinematics.Simon M. Huttegger - 2015 - Review of Symbolic Logic 8 (4):611-648.
    We explore the question of whether sustained rational disagreement is possible from a broadly Bayesian perspective. The setting is one where agents update on the same information, with special consideration being given to the case of uncertain information. The classical merging of opinions theorem of Blackwell and Dubins shows when updated beliefs come and stay closer for Bayesian conditioning. We extend this result to a type of Jeffrey conditioning where agents update on evidence that is uncertain but solid. However, merging (...)
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  • Bayesian conditionalization and probability kinematics.Colin Howson & Allan Franklin - 1994 - British Journal for the Philosophy of Science 45 (2):451-466.
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  • In Defence of Objective Bayesianism.Patryk Dziurosz-Serafinowicz - 2012 - International Studies in the Philosophy of Science 26 (3):348-351.
    International Studies in the Philosophy of Science, Volume 26, Issue 3, Page 348-351, September 2012.
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  • Foundations of Probability.Rachael Briggs - 2015 - Journal of Philosophical Logic 44 (6):625-640.
    The foundations of probability are viewed through the lens of the subjectivist interpretation. This article surveys conditional probability, arguments for probabilism, probability dynamics, and the evidential and subjective interpretations of probability.
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  • Historical and Conceptual Foundations of Information Physics.Anta Javier - 2021 - Dissertation, Universitat de Barcelona
    The main objective of this dissertation is to philosophically assess how the use of informational concepts in the field of classical thermostatistical physics has historically evolved from the late 1940s to the present day. I will first analyze in depth the main notions that form the conceptual basis on which 'informational physics' historically unfolded, encompassing (i) different entropy, probability and information notions, (ii) their multiple interpretative variations, and (iii) the formal, numerical and semantic-interpretative relationships among them. In the following, I (...)
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