This book extends the range of applications of Shannon information theory and will be invaluable to scientists interested in communication, information and thermodynamics, structure and complexity, self-organization, pattern formation and recognition, statistics and fuzzy set theory.

Inhalt

1. Relative Information What For?.- 1.1 Information Theory, What Is It?.- 1.1.1 Summary of the Story.- 1.1.2 Communication and Information.- 1.2 Information and Natural Language.- 1.2.1 Syntax, Semantics, Lexeme.- 1.2.2 Information, Learning, Dislearning.- 1.3 Prerequisites for a Theory of Information.- 1.3.1 Relativity of Information.- 1.3.2 Negative Information.- 1.3.3 Entropy of Form and Pattern.- 1.3.4 Information and Thermodynamics.- 1.3.5 Information and Subjectivity.- 1.4 Information and Systems.- 1.4.1 A Model of General Systems.- 1.4.2 A Model of Relative Information.- 1.4.3 A Few Comments.- 1.5 How We Shall Proceed.- 1.5.1 Aim of the Book.- 1.5.2 Subjective Information and Relative Information.- 1.5.3 Minkowskian Observation of Events.- 1.5.4 A Unified Approach to Discrete Entropy and Continuous Entropy.- 1.5.5 A Word of Caution to the Reader.- 2. Information Theory The State of the Art.- 2.1 Introduction.- 2.2 Shannon Measure of Uncertainty.- 2.2.1 The Probabilistic Framework.- 2.2.2 Shannon Informational Entropy.- 2.2.3 Entropy of Random Variables.- 2.3 An Intuitive Approach to Entropy.- 2.3.1 Uniform Random Experiments.- 2.3.2 Non Uniform Random Experiments.- 2.4 Conditional Entropy.- 2.4.1 Framework of Random Experiments.- 2.4.2 Application to Random Variables.- 2.5 A Few Properties of Discrete Entropy.- 2.6 Prior Characterization of Discrete Entropy.- 2.6.1 Properties of Uncertainty.- 2.6.2 Some Consequences of These Properties.- 2.7 The Concept of Information.- 2.7.1 Shannon Information.- 2.7.2 Some Properties of Transinformation.- 2.7.3 Transinformation of Random Variables.- 2.7.4 Remarks on the Notation.- 2.8 Conditional Transinformation.- 2.8.1 Main Definition.- 2.8.2 Some Properties of Conditional Transinformation.- 2.8.3 ConditionalTransinformation of Random Variables.- 2.9 Renyi Entropy.- 2.9.1 Definition of Renyi Entropy.- 2.9.2 Meaning of the Renyi Entropy.- 2.9.3 Some Properties of the Renyi Entropy.- 2.10 Cross-Entropy or Relative Entropy.- 2.10.1 The Main Definition.- 2.10.2 A Few Comments.- 2.11 Further Measures of Uncertainty.- 2.11.1 Entropy of Degree c.- 2.11.2 Quadratic Entropy.- 2.11.3 R norm Entropy.- 2.11.4 Effective Entropy.- 2.12 Entropies of Continuous Variables.- 2.12.1 Continuous Shannon Entropy.- 2.12.2 Some Properties of Continuous Entropy.- 2.12.3 Continuous Transinformation.- 2.12.4 Further Extensions.- 2.13 Hatori's Derivation of Continuous Entropy.- 2.14 Information Without Probability.- 2.14.1 A Functional Approach.- 2.14.2 Relative Information.- 2.15 Information and Possibility.- 2.15.1 A Few Prerequisites.- 2.15.2 A Measure of Uncertainty Without Probability.- 2.16 Conclusions.- 3. A Critical Review of Shannon Information Theory.- 3.1 Introduction.- 3.2 On the Invariance of Measures of Information.- 3.3 On the Modelling of Negative Transinformation.- 3.3.1 Classification of Terms.- 3.3.2 The Problem of Modelling True and False.- 3.3.3 Error-Detecting Codes.- 3.4 On the Symmetry of Transinformation.- 3.4.1 A Diverting Example.- 3.4.2 Application of Information Theory.- 3.4.3 On the Symmetry of Transinformation.- 3.4.4 On a Possible Application of Renyi Entropy.- 3.5 Entropy and the Central Limit Theorem.- 3.5.1 The Central Limit Theorem.- 3.5.2 An Information Theoretical Approach to the Central Limit Theorem.- 3.5.3 Relation with Thermodynamics.- 3.5.4 Continuous Entropy Versus Discrete Entropy.- 3.6 On the Entropy of Continuous Variables.- 3.6.1 The Sign of the Continuous Entropy.- 3.6.2 A Nice Property of Continuous Entropy.- 3.7 Arguments to SupportContinuous Entropy.- 3.7.1 On the Negativeness of Continuous Entropy.- 3.7.2 On the Non-invariance of Continuous Entropy.- 3.7.3 Channel Capacity in the Presence of Noise.- 3.8 The Maximum Entropy Principle.- 3.8.1 Statement of the Principle.- 3.8.2 Some Examples.- 3.9 Arguments to Support the Maximum Entropy Principle.- 3.9.1 Information Theoretical Considerations.- 3.9.2 Thermodynamic Considerations.- 3.9.3 Axiomatic Derivation.- 3.9.4 A Few Comments.- 3.10 Information, Syntax, Semantics.- 3.10.1 On the Absolute Nature of Information.- 3.10.2 Information and Natural Language.- 3.11 Information and Thermodynamics.- 3.11.1 Informational and Thermodynamic Entropy.- 3.11.2 Thermodynamic Entropy of Open Systems.- 3.12 Conclusions.- 4. A Theory of Relative Information.- 4.1 Introduction.- 4.2 Observation, Aggregation, Invariance.- 4.2.1 Principle of Aggregation.- 4.2.2 Principle of Invariance.- 4.2.3 A Few Comments.- 4.3 Observation with Informational Invariance.- 4.4 Euclidean Invariance.- 4.4.1 Orthogonal Transformation.- 4.4.2 Application to the Observation of Probabilities.- 4.4.3 Application to the Observation of Classes.- 4.5 Minkowskian Invariance.- 4.5.1 Lorentz Transformation.- 4.5.2 Application to the Observation of Probabilities.- 4.5.3 Application to the Observation of Classes.- 4.6 Euclidean or Minkowskian Observation?.- 4.6.1 Selection of the Observation Mode.- 4.6.2 Application to the [Uncertainty, Information] Pair.- 4.7 Information Processes and Natural Languages.- 4.7.1 On the Absoluteness of Information.- 4.7.2 The Information Process.- 4.7.3 Natural Language.- 4.7.4 Information and Natural Language.- 4.8 Relative Informational Entropy.- 4.8.1 Introduction to the Relative Observation.- 4.8.2 Informational Invariance of the Observation.- 4.8.3 Relative Entropy.- 4.8.4 Comments and Remarks.- 4.9 Conditional Relative Entropy.- 4.9.1 Relative Entropy of Product of Messages.- 4.9.2 Composition Law for Cascaded Observers.- 4.9.3 Relative Entropy Conditional to a Given Experiment.- 4.9.4 Applications to Determinacy.- 4.9.5 Comparison of H(?/?) with Hr(?/?).- 4.10 On the Meaning and the Estimation of the Observation Parameter.- 4.10.1 Estimation of the Observation Parameter.- 4.10.2 Practical Meaning of the Observation Parameter.- 4.10.3 On the Value of the Observation Parameter u(R).- 4.11 Relative Transinformation.- 4.11.1 Derivation of Relative Transinformation.- 4.11.2 Some Properties of the Relative Transinformation.- 4.11.3 Relative Entropy and Information Balance.- 4.11.4 Application to the Encoding Problem.- 4.12 Minkowskian Relative Transinformation.- 4.12.1 Definition of Minkowskian Relative Transinformation.- 4.12.2 Some Properties of Minkowskian Relative Transinformation.- 4.12.3 Identification via Information Balance.- 4.13 Effect of Scaling Factor in an Observation with Informational Invariance.- 4.14 Comparison with Renyi Entropy.- 4.14.1 Renyi Entropy and Relative Entropy.- 4.14.2 Transinformation of Order c and Relative Transinformation.- 5. A Theory of Subjective Information.- 5.1 Introduction.- 5.2 Subjective Entropy.- 5.2.1 Definition of Subjective Entropy.- 5.2.2 A Few Remarks.- 5.3 Conditional Subjective Entropy.- 5.3.1 Definition of Conditional Subjective Entropy.- 5.3.2 Application to Determinacy.- 5.3.3 A Basic Inequality.- 5.4 Subjective Transinformation.- 5.4.1 Introduction.- 5.4.2 Subjective Transinformation.- 5.4.3 A Few Properties of Subjective Transinformation.- 5.4.4 Application to Independent Random Experiments.- 5.5 Conditional Subjective Transinformation.- 5.5.1 Definition.- 5.5.2 A FewProperties of Subjective Conditional Transinformation.- 5.6 Information Balance.- 5.6.1 Optimum Conditions for Information Balance.- 5.6.2 Non-optimum Conditions for Information Balance.- 5.7 Explicit Expression of Subjective Transinformation.- 5.7.1 Discrete Probability.- 5.7.2 Continuous Probability.- 5.8 The General Coding Problem.- 5.8.1 Preliminary Remarks.- 5.8.2 The General Coding Problem.- 5.8.3 On the Problem of Error Correcting Codes Revisited.- 5.9 Capacity of a Channel.- 5.9.1 The General Model.- 5.9.2 Channel with Noise.-…