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Lu Chenguang - One of the best experts on this subject based on the ideXlab platform.
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Using the Semantic Information G Measure to Explain and Extend Rate-Distortion Functions and Maximum Entropy Distributions
'MDPI AG', 2021Co-Authors: Lu ChenguangAbstract:In the rate-distortion function and the Maximum Entropy (ME) method, Minimum Mutual In-formation (MMI) distributions and ME distributions are expressed by Bayes-like formulas, in-cluding Negative Exponential Functions (NEFs) and partition functions. Why do these non-Probability functions exist in Bayes-like formulas? On the other hand, the rate-distortion function has three disadvantages: (1) the distortion function is subjectively defined; (2) the defi-nition of the distortion function between instances and labels is often difficult; (3) it cannot be used for data compression according to the labels' semantic meanings. The author has proposed using the semantic information G measure with both statistical Probability and Logical Probability before. We can now explain NEFs as truth functions, partition functions as Logical probabilities, Bayes-like formulas as semantic Bayes' formulas, MMI as Semantic Mutual Information (SMI), and ME as extreme ME minus SMI. In overcoming the above disadvantages, this paper sets up the relationship between truth functions and distortion functions, obtains truth functions from samples by machine learning, and constructs constraint conditions with truth functions to extend rate-distortion functions. Two examples are used to help readers understand the MMI iteration and to support the theoretical results. Using truth functions and the semantic information G measure, we can combine machine learning and data compression, including semantic com-pression. We need further studies to explore general data compression and recovery, according to the semantic meaning.Comment: 22 pages, 5 figure
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Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox
2020Co-Authors: Lu ChenguangAbstract:After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of Logical Probability as the confirmation measure. So far, many confirmation measures have been proposed. Measure F proposed by Kemeny and Oppenheim among them possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the semantic information theory, a measure b* similar to F is derived from the medical test. Like the likelihood ratio, measures b* and F can only indicate the quality of channels or the testing means instead of the quality of Probability predictions. Furthermore, it is still not easy to use b*, F, or another measure to clarify the Raven Paradox. For this reason, measure c* similar to the correct rate is derived. Measure c* supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the Raven Paradox. An example indicates that measures F and b* are helpful for diagnosing the infection of Novel Coronavirus, whereas most popular confirmation measures are not. Another example reveals that all popular confirmation measures cannot be used to explain that a black raven can confirm “Ravens are black” more strongly than a piece of chalk. Measures F, b*, and c* indicate that the existence of fewer counterexamples is more important than more positive examples’ existence, and hence, are compatible with Popper’s falsification thought
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Channels' Confirmation and Predictions' Confirmation: from the Medical Test to the Raven Paradox
'MDPI AG', 2020Co-Authors: Lu ChenguangAbstract:After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple discovered the Raven Paradox (RP). Then, Carnap used the Logical Probability increment as the confirmation measure. So far, many confirmation measures have been proposed. Measure F among them proposed by Kemeny and Oppenheim possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the semantic information theory, a measure b* similar to F is derived from the medical test. Like the likelihood ratio, b* and F can only indicate the quality of channels or the testing means instead of the quality of Probability predictions. And, it is still not easy to use b*, F, or another measure to clarify the RP. For this reason, measure c* similar to the correct rate is derived. The c* has the simple form: (a-c)/max(a, c); it supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the RP. Some examples are provided to show why it is difficult to use one of popular confirmation measures to eliminate the RP. Measure F, b*, and c* indicate that fewer counterexamples' existence is more essential than more positive examples' existence, and hence, are compatible with Popper's falsification thought.Comment: 12 tables, 7 figure
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From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning
2018Co-Authors: Lu ChenguangAbstract:A group of transition Probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. By the third kind of Bayes' theorem, we can directly convert a Shannon's channel into an optimized semantic channel. When a sample is not big enough, we can use a truth function with parameters to produce the likelihood function, then train the truth function by the conditional sampling distribution. The third kind of Bayes' theorem is proved. A semantic information theory is simply introduced. The semantic information measure reflects Popper's hypothesis-testing thought. The Semantic Information Method (SIM) adheres to maximum semantic information criterion which is compatible with maximum likelihood criterion and Regularized Least Squares criterion. It supports Wittgenstein's view: the meaning of a word lies in its use. Letting the two channels mutually match, we obtain the Channels' Matching (CM) algorithm for machine learning. The CM algorithm is used to explain the evolution of the semantic meaning of natural language, such as "Old age". The semantic channel for medical tests and the confirmation measures of test-positive and test-negative are discussed. The applications of the CM algorithm to semi-supervised learning and non-supervised learning are simply introduced. As a predictive model, the semantic channel fits variable sources and hence can overcome class-imbalance problem. The SIM strictly distinguishes statistical Probability and Logical Probability and uses both at the same time. This method is compatible with the thoughts of Bayes, Fisher, Shannon, Zadeh, Tarski, Davidson, Wittgenstein, and Popper.It is a competitive alternative to Bayesian inference.Comment: 17 pages,7 figure
Michael Emmett Brady - One of the best experts on this subject based on the ideXlab platform.
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j m keynes and e borel s initial skipping of part ii of the a treatise on Probability in his 1924 review what changed borel s mind 15 years later
Social Science Research Network, 2020Co-Authors: Michael Emmett BradyAbstract:Emile Borel’s review of the A Treatise on Probability in 1924 is, in my opinion, quite above average. I would give it a grade of B/B+. Borel was also an intellectually honest researcher. Borel did not pretend to have read Parts II, III, IV, and V of Keynes’s A Treatise on Probability, as has been done repeatedly by psychologists, philosophers, historians, and economists, who have cited the A Treatise on Probability in their references when writing about Keynes’s Logical theory of Probability in his A Treatise on Probability. Borel apologizes to Keynes (and Bertrand Russell,who Borel knew had assisted Keynes in writing the A Treatise on Probability) for not reading Part II of Keynes’s A Treatise on Probability because he realized that, for Keynes, Part II was the most important part of the A Treatise on Probability. Borel was correct. It was the most important and intellectually powerful part of the book. It was the most important and intellectually powerful part of the book because Keynes presented for the second time in history a theoretical, technically advanced approach to imprecise Probability. The first attempt in history was Boole’s original achievement in The Laws of Thought in 1854. Adam Smith had presented the first non technically advanced imprecise theory of Probability in 1776 in the Wealth of Nations, which was opposed by Jeremy Bentham’s precise theory of Probability that was used in his 1787 The Principles of Morals and Legislation. However, Borel bemoaned the fact that Maxwell, who was a graduate, just like Keynes himself, of Cambridge University, who had made contributions to physics using the limiting frequency interpretation of Probability, which Borel thought that Keynes had given insufficient space and emphasis to in his book, had been overlooked by Keynes. This is correct with respect to Part I of the A Treatise on Probability. However,it is incorrect with respect to the totality of A Treatise on Probability because Keynes covered Maxwell on pp.172-174 of Chapter 16 in Part II. Maxwell is listed in the index to A Treatise on Probability on p.463. In Part V, in chapter 32, Keynes makes it clear that, if the only relevant evidence consists of statistical frequencies and there is no other relevant evidence, then the Logical Probability estimate of a Probability is identical to the estimate made by the limiting frequency theory if the statistical frequency can be shown to be stable over time using the Lexis -Q test. Another important result of this paper is that it appears that no academic has read Part II of the A Treatise on Probability since 1921. Otherwise, it should already have become common knowledge that Keynes had covered Maxwell in chapter 16 of Part II of the A Treatise on Probability.
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an examination of the very severe ignorance of keynes s a treatise on Probability among heterodox economists and their erroneous beliefs about Logical and subjective Probability
Social Science Research Network, 2019Co-Authors: Michael Emmett BradyAbstract:Heterodox economists have simply skipped the two most important parts of Keynes’s A Treatise on Probability (1921), Part II and Part V. They basically assess Keynes’s position on Probability and uncertainty based on a reading primarily of Chapter III of Part I of the A Treatise on Probability. This results in their failure to grasp Keynes’s inexact measurement – approximation approach to Probability in Part II and Keynes’s inexact measurement – approximation approach to statistics in Part V of the TP. Both Part II and V form the basic foundation of Keynes’s approach to Logical Probability that Keynes built on Boole. Specifically, heterodox economists are ignorant of (i) Keynes’s inexact approach to measurement, based on Boolean approximation that uses lower and upper bounds, when dealing with Probability and (ii) Keynes’s inexact approach to measurement ,based on Boolean approximation that uses lower and upper bounds, when dealing with statistics. This results in a belief that Keynes’s method involved an application of ordinal Probability only some of the time, because there was supposed to be entities, called non-comparable, non-measureable and incommensurable probabilities, that can’t be analyzed. Heterodox confusions about Keynes’s discussion on pp. 31-36 of the TP concerning unknown probabilities and indeterminate probabilities, where indeterminate probabilities are Boolean in nature and have nothing to do with unknown probabilities, leads heterodox economists into an intellectual quagmire of quicksand that could have been easily avoided if they had covered Parts II and V of A Treatise on Probability. The extensive, but unique, Keynes–Townshend correspondence over the connections between the GT and TP in 1937-38 showed why Keynes’s method of inexact measurement and approximation for both Probability and statistics is what links the GT and TP. On questions of Probability and expectations, only the TP and the GT are mentioned by Keynes and Townshend in their correspondence. There is no mention of the 1937 QJE article or of fundamental uncertainty or of Frank Ramsey or subjective Probability. An examination of both Rosser and Skidelsky reveals that they both simply have no basic understanding about what a Logical theory of Probability is or what a subjective theory of Probability is. Both Rosser and Skidelsky, like Muth before them, confuse the two approaches. An examination of Rosser and Skidelsky illustrates the astounding ignorance of Keynes’s A Treatise on Probability on the part of heterodox economists. Neither Rosser or Skidelsky demonstrate that they have even the slightest understanding of what an interval valued Probability is, what Keynes’s method was-inexact measurement and approximation, how uncertainty is related to non additivity, that Keynes was not a subjectivist, that Savage rejected the frequency approach to Probability, and that subjective Probability distributions can’t possibly converge to an objective Probability distribution because objective Probability does not exist according to de Finetti.
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keynes always adhered to his Logical objective Probability relation defined as p a h equals a rational degree of belief α Logical Probability always remained the guide to life for j m keynes
Social Science Research Network, 2019Co-Authors: Michael Emmett BradyAbstract:Keynes’s 1931 acknowledgement, that Ramsey’s theory of subjective degree of belief, based on numerically precise Probability, was acceptable to him in the special case where w=1, has been constantly misinterpreted. This misinterpretation follows from the lack of understanding of Keynes's weight of the argument relation. This required that Keynes’s second Logical relation of the A Treatise on Probability, the evidential weight of the argument, V(a/H)=w,0≤w≤1, where w=K/(K+I) and K defined the amount of relevant knowledge and I defined the amount of relevant ignorance, was defined and explicitly taken into account. It has been completely overlooked by all commentators that Keynes also stated in the same comment in 1931 that Ramsey’s theory did not deal with Keynes’s rational degrees of belief, P(a/h)=α,where 0≤α≤1. Only in the special case where w=1 does Keynes accept Ramsey’s approach because then the lower Probability also equals the upper Probability, which means that you now have additive, precise numerically definite probabilities. Keynes conceded to Ramsey what he had always agree about, that the purely mathematical laws of the Probability calculus can be interpreted as coherence constraints requiring that the probabilities of rational decision makers must be consistent with the assumption of additivity if, and only if, w=1. The literature on Keynes’s Logical Probability relation, P, has failed to grasp Keynes’s very clear statements supporting it.
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given keynes s definitions of Logical Probability and evidential weight it is impossible for keynes s approach to measurement to be an ordinal theory his non numerical probabilities must be based on inexact and imprecise measurement using approximati
Social Science Research Network, 2019Co-Authors: Michael Emmett BradyAbstract:Keynes spent all of his introductory Chapter One of the A Treatise on Probability emphasizing that his Logical relation of Probability (a relation of similarity – dissimilarity based on analogy) P(a/h)=α, where α came in degrees and 0≤α≤1. In chapter 6, Keynes introduced his Logical relation of the evidential weight of the argument, V(a/h), but did not specify what it equaled, although he clearly stated that he would integrate both Probability and weight together later. In chapter 26, Keynes specified that V(a/h)= w, where we came in degrees and 0≤w≤1. Keynes showed how to integrate both Probability and weight together in his conventional coefficient of weight and risk, c, in chapter 26 of the A Treatise on Probability. Probability and weight were also integrated together by Keynes using the theory of inexact measurement or approximation based on intervals with upper and lower bounds or limits. Ordinal Probability can do none of these things. Advocates of the claim that Keynes’s theory was an ordinal theory of Probability concentrate exclusively on a misinterpreted diagram that appears near the end of chapter 3 of the A Treatise on Probability. Thus, while Keynes’s theory can easily deal with ordinal Probability with the aid of the Principle of Indifference, it is not part of Keynes’s main or major analysis in the A Treatise on Probability, which was interval Probability. The same conclusion holds for numerical Probability and rational expectations. Rational expectations are a very special case that occurs if w=1. The heterodox, institutionalist, or Post Keynesian approaches, involving irreducible, fundamental, or radical uncertainty, are a very, very special case that occurs only if w=0 in the very long run. Their argument, that measurement is not possible at all, be it exact or inexact, is rejected by Keynes in chapter 4 of the General Theory. Thus, while Keynes’s theory can easily deal with ordinal Probability, it is impossible for the ordinal Probability to deal with inexact and imprecise interval-valued Probability measurement. Keynes’s theory of measurement involves interval-valued Probability in the A Treatise on Probability with the numerical and ordinal Probability being very special cases of his general theory of interval-valued Probability. Keynes’s approach to measurement in the General Theory also involved inexact and imprecise measurement as deployed by Keynes in chapter 4 of the General Theory. No ordinal Probability approach to measurement is used by Keynes in the General Theory. The great value of the 1936-1938 Keynes-Townshend exchanges is that they show that Keynes presented an approach to measurement that was imprecise and inexact in both the A Treatise on Probability and the General Theory.
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how should the post keynesian school define uncertainty 1 the only correct answer is to use keynes s own definition given in footnote 1 on page 148 of chapter 12 of the general theory uncertainty is an inverse function of the weight of the argument
Social Science Research Network, 2016Co-Authors: Michael Emmett BradyAbstract:The Post Keynesian, Institutionalist and Heterodox schools of economics have failed for 83 years to discern the definition of uncertainty given by Keynes in footnote 1 on page 148 of the General Theory that was repeated on page 240 of the General Theory. The footnotes on page 148 and 240 of the General Theory are the foundation for Townshend’s summary of the 1937-1938 Keynes-Townshend correspondence, which was that Keynes’s non numerical (interval valued) probabilities and weight of the evidence (argument) from the A Treatise on Probability were the foundation for Keynes’s liquidity preference theory of the rate of interest in the General Theory. Keynes’s reply to Townshend was that there was very little of Townshend’s summary from which he would differ and that Keynes’s theory (“…my theory…”) was the theory presented in the A Treatise on Probability. Nowhere in the 1937-38 exchanges between Keynes and Townshend is there any mention of F.Ramsey’s objections to Keynes theory of Logical Probability or of the February,1937 Quarterly Journal of Economics reply article in which Post Keynesians claim that Keynes gave a new definition of uncertainty as ‘fundamental uncertainty.'
Jan Wolenski - One of the best experts on this subject based on the ideXlab platform.
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the lvov warsaw school and contemporary philosophy
1998Co-Authors: Katarzyna Kijaniaplacek, Jan WolenskiAbstract:Preface. Introduction. The Reception of the Lvov-Warsaw School J. Wolenski. Part I: History and Comparisons. Twardowski's Distinction Between Actions and Products J. Brandl. On Ajdukiewicz's Empirical Meaning- Rule and Wittgenstein's Defining Criterion T. Czarnecki. Inspirations and Controversies: From the Letters Between K. Twardowski and A. Meinong R. Jadczak. The Lvov-Warsaw School - the First School of Non-Positivist Scientific and Analytic Philosophy W. Krajewski. Women's Contributions to the Achievements of the Lvov-Warsaw School: A Survey E. Pakszys. Truth-Bearers from Twardowski to Tarski A. Rojszczak. Twardowski and Husserl on Wholes and Parts M. Rosiak. The Rationalistic Paradigm of Franz Brentano and Kazimierz Twardowski E.G. Vinogradov. Lukasiewicz's Interpretation of Aristotle's Concept of Possibility U. Zeglen. Part II: Lesniewski. De Veritate: Another Chapter. The Bolzano-Lesniewski Connection A. Betti. Lesniewski's Conception of Logic R. Poli, M. Libardi. Non-Elementary Exegesis of Twardowski's Theory of Presentation V.L. Vasiukov. On Some Essential Subsystems of Lesniewski's Ontology and the Equivalence Between the Singular Barbara and the Law of Leibniz in Ontology T. Waragai. Part III: Philosophy of Language. The Paradox of Grelling and Nelson Presented as a Veridical Observation Concerning Naming A. Grzegorczyk. Dambska, Quine and the So-Called Empty Names M. Marsonet. Truth and Time K. Misiuna. The Postulate of Precision: Its Sense and Its Limits M. Przelecki. Polish Logic, Language and Philosophy of Language R. Zuber. Part IV: Logic and the Foundations of Mathematics. The Ajdukiewicz Calculus, Polish Notationand Hilbert-Style Proofs W. Buszkowski. Jaskowski and Gentzen Approaches to Natural Deduction and Related Systems A. Indrzejczak. The Contribution of Polish Logicians to Recursion Theory R. Murawski. Studying Incompleteness of Information: A Class of Information Logics E. Orlowska. Part V: Ontology, Epistemology, Philosophy of Science. On Lukasiewicz's Theory of Probability T. Childers, O. Majer. On What There Is Not -- A Vindication of Reism J. Czerniawski. On the Concept of a Subject of Cognition in Ajdukiewicz's Philosophy A. Kanik. Induction and Probability in the Lvov-Warsaw School I. Niiniluoto. Lukasiewicz's Logical Probability and a Puzzle About Conditionalization T. Placek. Part VI: Logic and Philosophy. Truth as Consensus. A Logical Analysis K. Kijania-Placek. The Lvov-Warsaw School and the Problem of a Logical Formalism for General Systems Theory A.I. Uyemov. From Closure-Operatic Deductive Methodology to Non-Standard Alternatives S.J. Surma. Forgotten and Neglected Solutions of Problems in Philosophical Logic P. Weingartner. Index of Names.
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the lvov warsaw school and contemporary philosophy
1998Co-Authors: Katarzyna Kijaniaplacek, Jan WolenskiAbstract:Preface. Introduction. The Reception of the Lvov-Warsaw School J. Wolenski. Part I: History and Comparisons. Twardowski's Distinction Between Actions and Products J. Brandl. On Ajdukiewicz's Empirical Meaning- Rule and Wittgenstein's Defining Criterion T. Czarnecki. Inspirations and Controversies: From the Letters Between K. Twardowski and A. Meinong R. Jadczak. The Lvov-Warsaw School - the First School of Non-Positivist Scientific and Analytic Philosophy W. Krajewski. Women's Contributions to the Achievements of the Lvov-Warsaw School: A Survey E. Pakszys. Truth-Bearers from Twardowski to Tarski A. Rojszczak. Twardowski and Husserl on Wholes and Parts M. Rosiak. The Rationalistic Paradigm of Franz Brentano and Kazimierz Twardowski E.G. Vinogradov. Lukasiewicz's Interpretation of Aristotle's Concept of Possibility U. Zeglen. Part II: Lesniewski. De Veritate: Another Chapter. The Bolzano-Lesniewski Connection A. Betti. Lesniewski's Conception of Logic R. Poli, M. Libardi. Non-Elementary Exegesis of Twardowski's Theory of Presentation V.L. Vasiukov. On Some Essential Subsystems of Lesniewski's Ontology and the Equivalence Between the Singular Barbara and the Law of Leibniz in Ontology T. Waragai. Part III: Philosophy of Language. The Paradox of Grelling and Nelson Presented as a Veridical Observation Concerning Naming A. Grzegorczyk. Dambska, Quine and the So-Called Empty Names M. Marsonet. Truth and Time K. Misiuna. The Postulate of Precision: Its Sense and Its Limits M. Przelecki. Polish Logic, Language and Philosophy of Language R. Zuber. Part IV: Logic and the Foundations of Mathematics. The Ajdukiewicz Calculus, Polish Notationand Hilbert-Style Proofs W. Buszkowski. Jaskowski and Gentzen Approaches to Natural Deduction and Related Systems A. Indrzejczak. The Contribution of Polish Logicians to Recursion Theory R. Murawski. Studying Incompleteness of Information: A Class of Information Logics E. Orlowska. Part V: Ontology, Epistemology, Philosophy of Science. On Lukasiewicz's Theory of Probability T. Childers, O. Majer. On What There Is Not -- A Vindication of Reism J. Czerniawski. On the Concept of a Subject of Cognition in Ajdukiewicz's Philosophy A. Kanik. Induction and Probability in the Lvov-Warsaw School I. Niiniluoto. Lukasiewicz's Logical Probability and a Puzzle About Conditionalization T. Placek. Part VI: Logic and Philosophy. Truth as Consensus. A Logical Analysis K. Kijania-Placek. The Lvov-Warsaw School and the Problem of a Logical Formalism for General Systems Theory A.I. Uyemov. From Closure-Operatic Deductive Methodology to Non-Standard Alternatives S.J. Surma. Forgotten and Neglected Solutions of Problems in Philosophical Logic P. Weingartner. Index of Names.
David Ellerman - One of the best experts on this subject based on the ideXlab platform.
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Logical entropy introduction to classical and quantum Logical information theory
Social Science Research Network, 2018Co-Authors: David EllermanAbstract:Logical information theory is the quantitative version of the logic of partitions just as Logical Probability theory is the quantitative version of the dual Boolean logic of subsets. The resulting notion of information is about distinctions, differences and distinguishability and is formalized using the distinctions (“dits”) of a partition (a pair of points distinguished by the partition). All the definitions of simple, joint, conditional and mutual entropy of Shannon information theory are derived by a uniform transformation from the corresponding definitions at the Logical level. The purpose of this paper is to give the direct generalization to quantum Logical information theory that similarly focuses on the pairs of eigenstates distinguished by an observable, i.e., qudits of an observable. The fundamental theorem for quantum Logical entropy and measurement establishes a direct quantitative connection between the increase in quantum Logical entropy due to a projective measurement and the eigenstates (cohered together in the pure superposition state being measured) that are distinguished by the measurement (decohered in the post-measurement mixed state). Both the classical and quantum versions of Logical entropy have simple interpretations as “two-draw” probabilities for distinctions. The conclusion is that quantum Logical entropy is the simple and natural notion of information for quantum information theory focusing on the distinguishing of quantum states.
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what can partition logic contribute to information theory
arXiv: Information Theory, 2016Co-Authors: David EllermanAbstract:Logical Probability theory was developed as a quantitative measure based on Boole's logic of subsets. But information theory was developed into a mature theory by Claude Shannon with no such connection to logic. A recent development in logic changes this situation. In category theory, the notion of a subset is dual to the notion of a quotient set or partition, and recently the logic of partitions has been developed in a parallel relationship to the Boolean logic of subsets (subset logic is usually mis-specified as the special case of propositional logic). What then is the quantitative measure based on partition logic in the same sense that Logical Probability theory is based on subset logic? It is a measure of information that is named "Logical entropy" in view of that Logical basis. This paper develops the notion of Logical entropy and the basic notions of the resulting Logical information theory. Then an extensive comparison is made with the corresponding notions based on Shannon entropy.
Katarzyna Kijaniaplacek - One of the best experts on this subject based on the ideXlab platform.
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the lvov warsaw school and contemporary philosophy
1998Co-Authors: Katarzyna Kijaniaplacek, Jan WolenskiAbstract:Preface. Introduction. The Reception of the Lvov-Warsaw School J. Wolenski. Part I: History and Comparisons. Twardowski's Distinction Between Actions and Products J. Brandl. On Ajdukiewicz's Empirical Meaning- Rule and Wittgenstein's Defining Criterion T. Czarnecki. Inspirations and Controversies: From the Letters Between K. Twardowski and A. Meinong R. Jadczak. The Lvov-Warsaw School - the First School of Non-Positivist Scientific and Analytic Philosophy W. Krajewski. Women's Contributions to the Achievements of the Lvov-Warsaw School: A Survey E. Pakszys. Truth-Bearers from Twardowski to Tarski A. Rojszczak. Twardowski and Husserl on Wholes and Parts M. Rosiak. The Rationalistic Paradigm of Franz Brentano and Kazimierz Twardowski E.G. Vinogradov. Lukasiewicz's Interpretation of Aristotle's Concept of Possibility U. Zeglen. Part II: Lesniewski. De Veritate: Another Chapter. The Bolzano-Lesniewski Connection A. Betti. Lesniewski's Conception of Logic R. Poli, M. Libardi. Non-Elementary Exegesis of Twardowski's Theory of Presentation V.L. Vasiukov. On Some Essential Subsystems of Lesniewski's Ontology and the Equivalence Between the Singular Barbara and the Law of Leibniz in Ontology T. Waragai. Part III: Philosophy of Language. The Paradox of Grelling and Nelson Presented as a Veridical Observation Concerning Naming A. Grzegorczyk. Dambska, Quine and the So-Called Empty Names M. Marsonet. Truth and Time K. Misiuna. The Postulate of Precision: Its Sense and Its Limits M. Przelecki. Polish Logic, Language and Philosophy of Language R. Zuber. Part IV: Logic and the Foundations of Mathematics. The Ajdukiewicz Calculus, Polish Notationand Hilbert-Style Proofs W. Buszkowski. Jaskowski and Gentzen Approaches to Natural Deduction and Related Systems A. Indrzejczak. The Contribution of Polish Logicians to Recursion Theory R. Murawski. Studying Incompleteness of Information: A Class of Information Logics E. Orlowska. Part V: Ontology, Epistemology, Philosophy of Science. On Lukasiewicz's Theory of Probability T. Childers, O. Majer. On What There Is Not -- A Vindication of Reism J. Czerniawski. On the Concept of a Subject of Cognition in Ajdukiewicz's Philosophy A. Kanik. Induction and Probability in the Lvov-Warsaw School I. Niiniluoto. Lukasiewicz's Logical Probability and a Puzzle About Conditionalization T. Placek. Part VI: Logic and Philosophy. Truth as Consensus. A Logical Analysis K. Kijania-Placek. The Lvov-Warsaw School and the Problem of a Logical Formalism for General Systems Theory A.I. Uyemov. From Closure-Operatic Deductive Methodology to Non-Standard Alternatives S.J. Surma. Forgotten and Neglected Solutions of Problems in Philosophical Logic P. Weingartner. Index of Names.
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the lvov warsaw school and contemporary philosophy
1998Co-Authors: Katarzyna Kijaniaplacek, Jan WolenskiAbstract:Preface. Introduction. The Reception of the Lvov-Warsaw School J. Wolenski. Part I: History and Comparisons. Twardowski's Distinction Between Actions and Products J. Brandl. On Ajdukiewicz's Empirical Meaning- Rule and Wittgenstein's Defining Criterion T. Czarnecki. Inspirations and Controversies: From the Letters Between K. Twardowski and A. Meinong R. Jadczak. The Lvov-Warsaw School - the First School of Non-Positivist Scientific and Analytic Philosophy W. Krajewski. Women's Contributions to the Achievements of the Lvov-Warsaw School: A Survey E. Pakszys. Truth-Bearers from Twardowski to Tarski A. Rojszczak. Twardowski and Husserl on Wholes and Parts M. Rosiak. The Rationalistic Paradigm of Franz Brentano and Kazimierz Twardowski E.G. Vinogradov. Lukasiewicz's Interpretation of Aristotle's Concept of Possibility U. Zeglen. Part II: Lesniewski. De Veritate: Another Chapter. The Bolzano-Lesniewski Connection A. Betti. Lesniewski's Conception of Logic R. Poli, M. Libardi. Non-Elementary Exegesis of Twardowski's Theory of Presentation V.L. Vasiukov. On Some Essential Subsystems of Lesniewski's Ontology and the Equivalence Between the Singular Barbara and the Law of Leibniz in Ontology T. Waragai. Part III: Philosophy of Language. The Paradox of Grelling and Nelson Presented as a Veridical Observation Concerning Naming A. Grzegorczyk. Dambska, Quine and the So-Called Empty Names M. Marsonet. Truth and Time K. Misiuna. The Postulate of Precision: Its Sense and Its Limits M. Przelecki. Polish Logic, Language and Philosophy of Language R. Zuber. Part IV: Logic and the Foundations of Mathematics. The Ajdukiewicz Calculus, Polish Notationand Hilbert-Style Proofs W. Buszkowski. Jaskowski and Gentzen Approaches to Natural Deduction and Related Systems A. Indrzejczak. The Contribution of Polish Logicians to Recursion Theory R. Murawski. Studying Incompleteness of Information: A Class of Information Logics E. Orlowska. Part V: Ontology, Epistemology, Philosophy of Science. On Lukasiewicz's Theory of Probability T. Childers, O. Majer. On What There Is Not -- A Vindication of Reism J. Czerniawski. On the Concept of a Subject of Cognition in Ajdukiewicz's Philosophy A. Kanik. Induction and Probability in the Lvov-Warsaw School I. Niiniluoto. Lukasiewicz's Logical Probability and a Puzzle About Conditionalization T. Placek. Part VI: Logic and Philosophy. Truth as Consensus. A Logical Analysis K. Kijania-Placek. The Lvov-Warsaw School and the Problem of a Logical Formalism for General Systems Theory A.I. Uyemov. From Closure-Operatic Deductive Methodology to Non-Standard Alternatives S.J. Surma. Forgotten and Neglected Solutions of Problems in Philosophical Logic P. Weingartner. Index of Names.
