Download or read book Maximum Entropy and Information Theory Approaches to Economics written by Jason Smith and published by . This book was released on 2018 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the natural sciences, complex non-linear systems composed of large numbers of smaller subunits provide an opportunity to apply the tools of statistical mechanics and information theory. The principle of maximum entropy can usually provide shortcuts in the treatment of these complex systems. However, there is an impasse to straightforward application to social and economic systems: the lack of well-defined constraints for Lagrange multipliers. This is typically treated in economics by introducing marginal utility as a Lagrange multiplier.Jumping off from Gary Becker's 1962 paper "Irrational Behavior and Economic Theory" -- a maximum entropy argument in disguise -- we introduce Peter Fielitz and Guenter Borchardt's concept of "information equilibrium" presented in arXiv:0905.0610v4 [physics.gen-ph] as a means of applying maximum entropy methods even in cases where well-defined constraints such as energy conservation required to define Lagrange multipliers and partition functions are not obvious (i.e. economics). From these initial steps we are able to motivate a well-defined constraint in terms of growth rates and develop a formalism for ensembles of markets described by information equilibrium conditions. We apply information equilibrium to a description of the US unemployment rate, connect it to search and matching theory, and empirical regularities such as Okun's Law. This represents a step toward Lee Smolin's call for a "statistical economics" analogous to statistical mechanics in arXiv:0902.4274 [q-fin.GN].
Download or read book Information and Entropy Econometrics written by Amos Golan and published by Now Publishers Inc. This book was released on 2008 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information and Entropy Econometrics - A Review and Synthesis summarizes the basics of information theoretic methods in econometrics and the connecting theme among these methods. The sub-class of methods that treat the observed sample moments as stochastic is discussed in greater details. I Information and Entropy Econometrics - A Review and Synthesis -focuses on inter-connection between information theory, estimation and inference. -provides a detailed survey of information theoretic concepts and quantities used within econometrics and then show how these quantities are used within IEE. -pays special attention for the interpretation of these quantities and for describing the relationships between information theoretic estimators and traditional estimators. Readers need a basic knowledge of econometrics, but do not need prior knowledge of information theory. The survey is self contained and interested readers can replicate all results and examples provided. Whenever necessary the readers are referred to the relevant literature. Information and Entropy Econometrics - A Review and Synthesis will benefit researchers looking for a concise introduction to the basics of IEE and to acquire the basic tools necessary for using and understanding these methods. Applied researchers can use the book to learn improved new methods, and applications for extracting information from noisy and limited data and for learning from these data.
Download or read book Maximum Entropy Information Without Probability and Complex Fractals written by Guy Jumarie and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every thought is a throw of dice. Stephane Mallarme This book is the last one of a trilogy which reports a part of our research work over nearly thirty years (we discard our non-conventional results in automatic control theory and applications on the one hand, and fuzzy sets on the other), and its main key words are Information Theory, Entropy, Maximum Entropy Principle, Linguistics, Thermodynamics, Quantum Mechanics, Fractals, Fractional Brownian Motion, Stochastic Differential Equations of Order n, Stochastic Optimal Control, Computer Vision. Our obsession has been always the same: Shannon's information theory should play a basic role in the foundations of sciences, but subject to the condition that it be suitably generalized to allow us to deal with problems which are not necessarily related to communication engineering. With this objective in mind, two questions are of utmost importance: (i) How can we introduce meaning or significance of information in Shannon's information theory? (ii) How can we define and/or measure the amount of information involved in a form or a pattern without using a probabilistic scheme? It is obligatory to find suitable answers to these problems if we want to apply Shannon's theory to science with some chance of success. For instance, its use in biology has been very disappointing, for the very reason that the meaning of information is there of basic importance, and is not involved in this approach.
Download or read book The Maximum Entropy Method written by Nailong Wu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forty years ago, in 1957, the Principle of Maximum Entropy was first intro duced by Jaynes into the field of statistical mechanics. Since that seminal publication, this principle has been adopted in many areas of science and technology beyond its initial application. It is now found in spectral analysis, image restoration and a number of branches ofmathematics and physics, and has become better known as the Maximum Entropy Method (MEM). Today MEM is a powerful means to deal with ill-posed problems, and much research work is devoted to it. My own research in the area ofMEM started in 1980, when I was a grad uate student in the Department of Electrical Engineering at the University of Sydney, Australia. This research work was the basis of my Ph.D. the sis, The Maximum Entropy Method and Its Application in Radio Astronomy, completed in 1985. As well as continuing my research in MEM after graduation, I taught a course of the same name at the Graduate School, Chinese Academy of Sciences, Beijingfrom 1987to 1990. Delivering the course was theimpetus for developing a structured approach to the understanding of MEM and writing hundreds of pages of lecture notes.
Download or read book Maximum Entropy in Action written by Brian Buck and published by Oxford : Clarendon Press ; New York : Oxford University Press. This book was released on 1991 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of introductory, interdisciplinary articles and lectures covering the fundamentals of the maximum entropy approach, a powerful new technique that provides a much needed extension of the established principles of rational inference in the sciences. Maximum entropy allows the interpretation of incomplete and "noisy" data, providing a description of the underlying physical systems. It has found application in both practical and theoretical studies ranging from image enhancement to nuclear physics, and from statistical mechanics to economics. The work explores these applications with specific problems of data analysis taken from the physical sciences. It will interest all physical scientists who deal with data and its interpretation, including statisticians and statistical physicists.
Download or read book Econometrics of Information and Efficiency written by Jati Sengupta and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometrics as an applied discipline attempts to use information in a most efficient manner, yet the information theory and entropy approach developed by Shannon and others has not played much of a role in applied econometrics. Econometrics of Information and Efficiency bridges the gap. Broadly viewed, information theory analyzes the uncertainty of a given set of data and its probabilistic characteristics. Whereas the economic theory of information emphasizes the value of information to agents in a market, the entropy theory stresses the various aspects of imprecision of data and their interactions with the subjective decision processes. The tools of information theory, such as the maximum entropy principle, mutual information and the minimum discrepancy are useful in several areas of statistical inference, e.g., Bayesian estimation, expected maximum likelihood principle, the fuzzy statistical regression. This volume analyzes the applications of these tools of information theory to the most commonly used models in econometrics. The outstanding features of Econometrics of Information and Efficiency are: A critical survey of the uses of information theory in economics and econometrics; An integration of applied information theory and economic efficiency analysis; The development of a new economic hypothesis relating information theory to economic growth models; New lines of research are emphasized.
Download or read book Forecasting with Maximum Entropy Hb written by FORT and published by IOP ebooks. This book was released on 2022-11-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims at providing a unifying framework, based on Information Entropy and its maximization, to connect the phenomenology of evolutionary biology, community ecology, financial economics, and statistical physics. This more comprehensive view, besides providing further insight into problems, enables problem-solving strategies by applying proven methods in one discipline to formally similar problems in other areas. The book also proposes a forecasting method for important practical problems in these disciplines and is directed to researchers, students and practitioners working on modelling the dynamics of complex systems. The common thread is how the flux of information both controls and serves to predict the dynamics of complex systems. It is shown how maximizing the Shannon information entropy allows one to infer a central object controlling the dynamics of complex systems, such as ecosystems or markets. The resulting models, which are known as pairwise maximum-entropy models, can be used to infer interactions from data in a wide variety of systems. Here, two examples are analysed in detail. The first is an application to conservation ecology, namely the issue of providing early warning indicators of population crashes of species of trees in tropical forests. The second is about forecasting the market values of firms through evolutionary economics. An interesting lesson is that PME modelling often produces accurate predictions despite not incorporating explicit interaction mechanisms. Key features Written to be suitable for a broad spectrum of readers and assumes little mathematical specialism. Includes pedagogical features: Worked examples, case studies and summaries. The interdisciplinary approach builds bridges between disciplines. Oriented to solve practical problems. Includes a combination of analytical derivations and numerical simulations with experiments
Download or read book Spatial Analysis of Interacting Economies written by David F. Batten and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: 6. 2 Basic Model Characteristics 185 6. 3 A Closed Model Approach to Interregional Estimation 189 7 Towards an Integrated System of Models for National and Regional Development 205 7. 1 Introduction 205 7. 2 In Search of a Framework for Integration 207 7. 3 National Development Scenarios 222 7. 4 The National-Regional Interlace 231 7. 5 Regional Development Scenarios 236 7. 6 Concluding Remarks 244 Appendixes 253 A Basic Microstate Descriptions 253 B Incomplete Prior Information: A Simple Example 257 C Computing Capital Coefficients and Turnpike Solutions: The DYNIO Package 259 D Minimizing Information Losses in Simple Aggregation: Two Test Problems 274 E Computing Interregional and Intersectoral Flows: 276 References 287 Index 305 vi LIST OF FIGURES 1. 1 A Three-Dimensional Guide to Later Chapters 12 2. 1 Historical Development of the Entropy Concept 32 2. 2 Selected Applications of Information Theory to Input-Output Analysis and Interaction Modelling 48 3. 1 The Bose-Einstein Analogy 58 5. 1 The Dog-Leg Input-Output Table 159 7. 1 A General Multilevel Social System 219 7. 2 The Hierarchical System of Models 219 7. 3 Choice of Production Techniques 230 7. 4 The National-Regional Interface 235 7. 5 A Sequential Compromise Procedure 243 7. 6 The Integrated Modelling System 246 vii LIST OF TABLES 3. 1 Production-Constrained Microstate Descriptions 59 3. 2 Production-Constrained Entropy Formulae 62 3. 3 Production-Constrained Solutions 65 3. 4 Doubly-Constrained Solutions 73 4. 1 The Static Input-Output Table 85 4.
Download or read book Maximum Entropy and Bayesian Methods written by G. Erickson and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume has its origin in the Seventeenth International Workshop on Maximum Entropy and Bayesian Methods, MAXENT 97. The workshop was held at Boise State University in Boise, Idaho, on August 4 -8, 1997. As in the past, the purpose of the workshop was to bring together researchers in different fields to present papers on applications of Bayesian methods (these include maximum entropy) in science, engineering, medicine, economics, and many other disciplines. Thanks to significant theoretical advances and the personal computer, much progress has been made since our first Workshop in 1981. As indicated by several papers in these proceedings, the subject has matured to a stage in which computational algorithms are the objects of interest, the thrust being on feasibility, efficiency and innovation. Though applications are proliferating at a staggering rate, some in areas that hardly existed a decade ago, it is pleasing that due attention is still being paid to foundations of the subject. The following list of descriptors, applicable to papers in this volume, gives a sense of its contents: deconvolution, inverse problems, instrument (point-spread) function, model comparison, multi sensor data fusion, image processing, tomography, reconstruction, deformable models, pattern recognition, classification and group analysis, segmentation/edge detection, brain shape, marginalization, algorithms, complexity, Ockham's razor as an inference tool, foundations of probability theory, symmetry, history of probability theory and computability. MAXENT 97 and these proceedings could not have been brought to final form without the support and help of a number of people.
Download or read book Maximum Entropy and Bayesian Methods Santa Barbara California U S A 1993 written by Glenn R. Heidbreder and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maximum entropy and Bayesian methods have fundamental, central roles in scientific inference, and, with the growing availability of computer power, are being successfully applied in an increasing number of applications in many disciplines. This volume contains selected papers presented at the Thirteenth International Workshop on Maximum Entropy and Bayesian Methods. It includes an extensive tutorial section, and a variety of contributions detailing application in the physical sciences, engineering, law, and economics. Audience: Researchers and other professionals whose work requires the application of practical statistical inference.
Download or read book Maximum Entropy Econometrics written by Amos Golan and published by John Wiley & Sons. This book was released on 1996-05 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph examines the problem of recovering and processing information when the underlying data are limited or partial, and the corresponding models that form the basis for estimation and inference are ill-posed or undermined
Download or read book Maximum Entropy and Ecology written by John Harte and published by OUP Oxford. This book was released on 2011-06-23 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This pioneering graduate textbook provides readers with the concepts and practical tools required to understand the maximum entropy principle, and apply it to an understanding of ecological patterns. Rather than building and combining mechanistic models of ecosystems, the approach is grounded in information theory and the logic of inference. Paralleling the derivation of thermodynamics from the maximum entropy principle, the state variable theory of ecology developed in this book predicts realistic forms for all metrics of ecology that describe patterns in the distribution, abundance, and energetics of species over multiple spatial scales, a wide range of habitats, and diverse taxonomic groups. The first part of the book is foundational, discussing the nature of theory, the relationship of ecology to other sciences, and the concept of the logic of inference. Subsequent sections present the fundamentals of macroecology and of maximum information entropy, starting from first principles. The core of the book integrates these fundamental principles, leading to the derivation and testing of the predictions of the maximum entropy theory of ecology (METE). A final section broadens the book's perspective by showing how METE can help clarify several major issues in conservation biology, placing it in context with other theories and highlighting avenues for future research.
Download or read book New Foundations for Information Theory written by David Ellerman and published by Springer Nature. This book was released on 2021-10-30 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph offers a new foundation for information theory that is based on the notion of information-as-distinctions, being directly measured by logical entropy, and on the re-quantification as Shannon entropy, which is the fundamental concept for the theory of coding and communications. Information is based on distinctions, differences, distinguishability, and diversity. Information sets are defined that express the distinctions made by a partition, e.g., the inverse-image of a random variable so they represent the pre-probability notion of information. Then logical entropy is a probability measure on the information sets, the probability that on two independent trials, a distinction or “dit” of the partition will be obtained. The formula for logical entropy is a new derivation of an old formula that goes back to the early twentieth century and has been re-derived many times in different contexts. As a probability measure, all the compound notions of joint, conditional, and mutual logical entropy are immediate. The Shannon entropy (which is not defined as a measure in the sense of measure theory) and its compound notions are then derived from a non-linear dit-to-bit transform that re-quantifies the distinctions of a random variable in terms of bits—so the Shannon entropy is the average number of binary distinctions or bits necessary to make all the distinctions of the random variable. And, using a linearization method, all the set concepts in this logical information theory naturally extend to vector spaces in general—and to Hilbert spaces in particular—for quantum logical information theory which provides the natural measure of the distinctions made in quantum measurement. Relatively short but dense in content, this work can be a reference to researchers and graduate students doing investigations in information theory, maximum entropy methods in physics, engineering, and statistics, and to all those with a special interest in a new approach to quantum information theory.
Download or read book Frontiers In Entropy Across The Disciplines Panorama Of Entropy Theory Computation And Applications written by M Zuhair Nashed and published by World Scientific. This book was released on 2022-08-30 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: Frontiers in Entropy Across the Disciplines presents a panorama of entropy emphasizing mathematical theory, physical and scientific significance, computational methods, and applications in mathematics, physics, statistics, engineering, biomedical signals, and signal processing.In the last century classical concepts of entropy were introduced in the areas of thermodynamics, information theory, probability theory, statistics, dynamical systems, and ergodic theory. During the past 50 years, dozens of new concepts of entropy have been introduced and studied in many disciplines. This volume captures significant developments in this arena. It features expository, review, and research papers by distinguished mathematicians and scientists from many disciplines. The level of mathematics ranges from intermediate level to research level. Each chapter contains a comprehensive list of references. Topics include entropy and society, entropy and time, Souriau entropy on symplectic model of statistical physics, new definitions of entropy, geometric theory of heat and information, maximum entropy in Bayesian networks, maximum entropy methods, entropy analysis of biomedical signals (review and comparison of methods), spectral entropy and its application to video coding and speech coding, a comprehensive review of 50 years of entropy in dynamics, a comprehensive review on entropy, entropy-like quantities and applications, topological entropy of multimodal maps, entropy production in complex systems, entropy production and convergence to equilibrium, reversibility and irreversibility in entropy, nonequilibrium entropy, index of various entropy, entropy and the greatest blunder ever.
Download or read book Non Extensive Entropy Econometrics for Low Frequency Series written by Second Bwanakare and published by De Gruyter Open. This book was released on 2017-07-24 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-extensive Entropy Econometrics for Low Frequency Series provides a new and robust power-law-based, non-extensive entropy econometrics approach to the economic modelling of ill-behaved inverse problems. Particular attention is paid to national account-based general equilibrium models known for their relative complexity. In theoretical terms, the approach generalizes Gibbs-Shannon-Golan entropy models, which are useful for describing ergodic phenomena. In essence, this entropy econometrics approach constitutes a junction of two distinct concepts: Jayne's maximum entropy principle and the Bayesian generalized method of moments. Rival econometric techniques are not conceptually adapted to solving complex inverse problems or are seriously limited when it comes to practical implementation. Recent literature showed that amplitude and frequency of macroeconomic fluctuations do not substantially diverge from many other extreme events, natural or human-related, once they are explained in the same time (or space) scale. Non-extensive entropy is a precious device for econometric modelling even in the case of low frequency series, since outputs evolving within the Gaussian attractor correspond to the Tsallis entropy limiting case of Tsallis q-parameter around unity. This book introduces a sub-discipline called Non-extensive Entropy Econometrics or, using a recent expression, Superstar Generalised Econometrics. It demonstrates, using national accounts-based models, that this approach facilitates solving nonlinear, complex inverse problems, previously considered intractable, such as the constant elasticity of substitution class of functions. This new proposed approach could extend the frontier of theoretical and applied econometrics.
Download or read book Maximum Entropy and Bayesian Methods in Inverse Problems written by C.R. Smith and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the text of the twenty-five papers presented at two workshops entitled Maximum-Entropy and Bayesian Methods in Applied Statistics, which were held at the University of Wyoming from June 8 to 10, 1981, and from August 9 to 11, 1982. The workshops were organized to bring together researchers from different fields to critically examine maxi mum-entropy and Bayesian methods in science, engineering, medicine, oceanography, economics, and other disciplines. An effort was made to maintain an informal environment where ideas could be easily ~xchanged. That the workshops were at least partially successful is borne out by the fact that there have been two succeeding workshops, and the upcoming Fifth Workshop promises to be the largest of all. These workshops and their proceedings could not have been brought to their final form without the substantial help of a number of people. The support of David Hofmann, the past chairman, and Glen Rebka, Jr. , the present chairman of the Physics Department of the University of Wyoming, has been strong and essential. Glen has taken a special interest in seeing that the proceedings have received the support required for their comple tion. The financial support of the Office of University Research Funds, University of Wyoming, is gratefully acknowledged. The secretarial staff, in particular Evelyn Haskell, Janice Gasaway, and Marce Mitchum, of the University of Wyoming Physics Department has contributed a great number of hours in helping C. Ray Smith organize and direct the workshops.
Download or read book Maximum Entropy and Bayesian Methods in Applied Statistics written by James H. Justice and published by Cambridge University Press. This book was released on 2009-01-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of papers by leading researchers in their respective fields contains contributions showing the use of the maximum entropy method in many of the fields in which it finds application. In the physical, mathematical and biological sciences it is often necessary to make inferences based on insufficient data. The problem of choosing one among the many possible conclusions or models which are compatible with the data may be resolved in a variety of ways. A particularly appealing method is to choose the solution which maximizes entropy in the sense that the conclusion or model honours the observed data but implies no further assumptions not warranted by the data. The maximum entropy principle has been growing in importance and acceptance in many fields, perhaps most notably statistical physics, astronomy, geophysics, signal processing, image analysis and physical chemistry. The papers included in this volume touch on most of the current areas of research activity and application, and will be of interest to research workers in all fields in which the maximum entropy method may be applied.
