Representations for real numbers and their ergodic properties

Preface 1. Introduction: Donsker's theorem, metric entropy and inequalities 2. Gaussian measures and processes sample continuity 3. Foundations of uniform central limit theorems: Donsker classes 4. Vapnik-Cervonenkis combinatorics 5. Measurability 6. Limit theorems for Vapnik-Cervonenkis and related classes 7. Metric entropy, with inclusion and bracketing 8. Approximation of functions and sets 9. Sums in general Banach spaces and invariance principles 10. Universal and uniform central limit theorems 11. The two-sample case, the bootstrap, and confidence sets 12. Classes of sets or functions too large for central limit theorems Appendices Subject index Author index Index of notation.

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Uniform Central Limit Theorems

SINCE the publication of our communication on the metric properties of the one-complement of Jaccard's similarity coefficient1, we have learned that this result was shown earlier by Marczewski and Steinhaus2. We regret the consequent redundancy of our paper. We tried to ensure its novelty by searching standard references and enquiring among mathematicians and statisticians, but apparently the earlier work, more extensive than ours, is not well known. We hope publication of our paper will have the positive effect of making it more well known. It is interesting that the earlier work also occurred in an ecological context3. A discussion of these and other properties of this useful function occurs in a recent contribution by Holgate4, who refers to it as the Marczewski–Steinhaus coefficient of similarity.

Distance between Sets

Information granulation and concept approximation are some of the fundamental issues of granular computing. Granulation of a universe involves grouping of similar elements into granules to form coarse-grained views of the universe. Approximation of concepts, represented by subsets of the universe, deals with the descriptions of concepts using granules. In the context of rough set theory, this paper examines the two related issues. The granulation structures used by standard rough set theory and the corresponding approximation structures are reviewed. Hierarchical granulation and approximation structures are studied, which results in stratified rough set approximations. A nested sequence of granulations induced by a set of nested equivalence relations leads to a nested sequence of rough set approximations. A multi-level granulation, characterized by a special class of equivalence relations, leads to a more general approximation structure. The notion of neighborhood systems is also explored.

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Information Granulation and Rough Set Approximation

1 Fundamental Concepts.- 1.1 Formulas.- 1.2 Propositions and Predicates.- 1.3 Sets.- 1.4 Operations on Sets.- 1.5 Partitions.- 1.6 Relations.- 1.7 Functions.- 1.8 Operations and Algebraic Systems.- 2 Boolean Algebras.- 2.1 Postulates for a Boolean Algebra.- 2.2 Examples of Boolean Algebras.- 2.2.1 The Algebra of Classes (Subsets of a Set).- 2.2.2 The Algebra of Propositional Functions.- 2.2.3 Arithmetic Boolean Algebras.- 2.2.4 The Two-Element Boolean Algebra.- 2.2.5 Summary of Examples.- 2.3 The Stone Representation Theorem.- 2.4 The Inclusion-Relation.- 2.4.1 Intervals.- 2.5 Some Useful Properties.- 2.6 n-Variable Boolean Formulas.- 2.7 n-Variable Boolean Functions.- 2.8 Boole's Expansion Theorem.- 2.9 The Minterm Canonical Form.- 2.9.1 Truth-tables.- 2.9.2 Maps.- 2.10 The Lowenheim-Muller Verification Theorem.- 2.11 Switching Functions.- 2.12 Incompletely-Specified Boolean Functions.- 2.13 Boolean Algebras of Boolean Functions.- 2.13.1 Free Boolean Algebras.- 2.14 Orthonormal Expansions.- 2.14.1 Lowenheim's Expansions.- 2.15 Boolean Quotient.- 2.16 The Boolean Derivative.- 2.17 Recursive Definition of Boolean Functions.- 2.18 What Good are "Big" Boolean Algebras?.- 3 The Blake Canonical Form.- 3.1 Definitions and Terminology.- 3.2 Syllogistic & Blake Canonical Formulas.- 3.3 Generation of BCF(f).- 3.4 Exhaustion of Implicants.- 3.5 Iterated Consensus.- 3.5.1 Quine's method.- 3.5.2 Successive extraction.- 3.6 Multiplication.- 3.6.1 Recursive multiplication.- 3.6.2 Combining multiplication and iterated consensus.- 3.6.3 Unwanted syllogistic formulas.- 4 Boolean Analysis.- 4.1 Review of Elementary Properties.- 4.2 Boolean Systems.- 4.2.1 Antecedent, Consequent, and Equivalent Systems.- 4.2.2 Solutions.- 4.3 Reduction.- 4.4 The Extended Verification Theorem.- 4.5 Poretsky's Law of Forms.- 4.6 Boolean Constraints.- 4.7 Elimination.- 4.8 Eliminants.- 4.9 Rudundant Variables.- 4.10 Substitution.- 4.11 The Tautology Problem.- 4.11.1 Testing for Tautology.- 4.11.2 The Sum-to-One Theorem.- 4.11.3 Nearly-Minimal SOP Formulas.- 5 Syllogistic Reasoning.- 5.1 The Principle of Assertion.- 5.2 Deduction by Consensus.- 5.3 Syllogistic Formulas.- 5.4 Clausal Form.- 5.5 Producing and Verifying Consequents.- 5.5.1 Producing Consequents.- 5.5.2 Verifying Consequents.- 5.5.3 Comparison of Clauses.- 5.6 Class-Logic.- 5.7 Selective Deduction.- 5.8 Functional Relations.- 5.9 Dependent Sets of Functions.- 5.10 Sum-to-One Subsets.- 5.11 Irredundant Formulas.- 6 Solution of Boolean Equations.- 6.1 Particular Solutions and Consistency.- 6.2 General Solutions.- 6.3 Subsumptive General Solutions.- 6.3.1 Successive Elimination.- 6.3.2 Deriving Eliminants from Maps.- 6.3.3 Recurrent Covers and Subsumptive Solutions.- 6.3.4 Simplified Subsumptive Solutions.- 6.3.5 Simplification via Marquand Diagrams.- 6.4 Parametric General Solutions.- 6.4.1 Successive Elimination.- 6.4.2 Parametric Solutions based on Recurrent Covers.- 6.4.3 Lowenheim's Formula.- 7 Functional Deduction.- 7.1 Functionally Deducible Arguments.- 7.2 Eliminable and Determining Subsets.- 7.2.1 u-Eliminable Subsets.- 7.2.2 u-Determining Subsets.- 7.2.3 Calculation of Minimal u-Determining Subsets.- 8 Boolean Identification.- 8.1 Parametric and Diagnostic Models.- 8.1.1 Parametric Models.- 8.1.2 The Diagnostic Axiom.- 8.1.3 Diagnostic Equations and Functions.- 8.1.4 Augmentation.- 8.2 Adaptive Identification.- 8.2.1 Initial and Terminal Specifications.- 8.2.2 Updating the Model.- 8.2.3 Effective Inputs.- 8.2.4 Test-Procedure.- 9 Recursive Realizations of Combinational Circuits.- 9.1 The Design-Process.- 9.2 Specifications.- 9.2.1 Specification-Formats.- 9.2.2 Consistent Specifications.- 9.3 Tabular Specifications.- 9.4 Strongly Combinational Solutions.- 9.5 Least-Cost Recursive Solutions.- 9.6 Constructing Recursive Solutions.- 9.6.1 The Procedure.- 9.6.2 An Implementation using BORIS.- A Syllogistic Formulas.- A.1 Absorptive Formulas.- A.2 Syllogistic Formulas.- A.3 Prime Implicants.- A.4 The Blake Canonical Form.

Boolean Reasoning: The Logic of Boolean Equations

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On a certain distance of sets and the corresponding distance of functions

Homogeneous operations and homogeneous algebras

/pdf/remarks-on-the-compactness-and-non-direct-products-of-2vpiqoc4xs.pdf

Remarks on the compactness and non direct products of measures

/pdf/independance-d-ensembles-et-prolongement-de-mesures-2boq9w5fcg.pdf

Indépendance d'ensembles et prolongement de mesures (Résultats et problèmes)

/pdf/independence-in-algebras-of-sets-and-boolean-algebras-3p9gs7ak05.pdf

Independence in algebras of sets and Boolean algebras

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