Dual Interpretation and Generalization of Entropy Maximizing Models in Regional Science.

by Netherlands Economic Institute.

Publisher: s.n in S.l

Written in English
Published: Downloads: 353
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Edition Notes

1

SeriesNetherlands Economic Institute Series: Foundations of Empirical Economic Research -- 73/07
ContributionsNijkamp, P., Paelinck, J.
ID Numbers
Open LibraryOL21784015M

Entropy is a concept that originated in thermodynamics, and later, via statistical mechanics, motivated entire branches of information theory, statistics, and machine learning.. Maximum entropy is the state of a physical system at greatest disorder or a statistical model of least encoded information, these being important theoretical analogs.. Maximum entropy may refer to. The maximum entropy principle was described detail in [1]. In this section, we only consider maximum entropy in terms of text classification. Given training data D = {(d1,c1), (d2,c2),, (dN,cN)} where di is list of context predicate, ci is class corresponding to di. Every real-valued function of the context and the class is a feature,fi (dc File Size: KB. $\begingroup$ The formulas involving entropy use the logarithm function. Since the base of the logarithms is usually taken to be $2$, entropy is measured in bits. If natural logarithms are used, the entropy is measured in nats. 3 The maximum entropy approach 4 3 The maximum entropy approach Goldwater and Johnson () compare StOT with Maximum Entropy models (or, as they are sometimes called, log-linear models) that are state of the art by now in computational linguistics (see for instance Berger, Della Pietra, and Della Pietra () or Abney ()).File Size: KB.

Improving predictability of time series using maximum entropy methods Gregor Chliamovitch,1,2, ∗ Bastien Chopard, 2Alexandre Dupuis, and Anton Golub2 1Department of Theoretical Physics, University of Geneva, Switzerland 2Department of Computer Science, University of Geneva, Switzerland (Dated: Septem ) We discuss how maximum entropy methods may be . Boltzmann-Gibbs entropy, in general. Here is a point, at which generalization of the ordinary maximum entropy method is expected to play a role. Now, the above-mentioned reasoning brings thermodynamics a new issue to be examined. That is, how robust the laws of thermodynamics are under such a generalization. The purpose. This site uses cookies for analytics, personalized content and ads. By continuing to browse this site, you agree to this use. Learn moreCited by: 4. The feature selection facility of the maximum entropy model learning makes it possible to find optimal case dependencies and optimal noun c!~ generalization levels. We describe the results of the experiment on learning probabilistic models of subcategorization preference f~om the EDR Japanese bracketed corpus.

Buy Entropy Theory in Hydraulic Engineering: An Introduction (Asce Press) by Vijay P. Singh (ISBN: ) from Amazon's Book Store. Everyday low . Entropy has also been applied in social science in the form of Social Entropy Theory (SET). Since entropy is a complex concept and has been widely applied in a variety of disciplines, it is helpful to identify the principal dimensions of the entropy concept, and to use these as criteria for the evaluation and interpretation of specific entropy. The book also contains a large number of examples. Unlike the examples in most books which are supplementary, the examples in this book are essential. This book can be used as a reference book or a textbook. For a two-semester course on information theory, this would be a suitable textbook for the rst semester. ENTROPY MAXIMISATION AND THE GRAVITY MODEL. The entropy maximisation technique, and its application to the derivation of the gravity model, are discussed. Wilson's family of gravity models is proposed for revision, and two other important conclusions are drawn; namely, the fact that the gravity model is entropy maximising should not endow it.

Dual Interpretation and Generalization of Entropy Maximizing Models in Regional Science. by Netherlands Economic Institute. Download PDF EPUB FB2

Berry, B. “Cities as Systems within Systems of Cities,”Papers and Proceedings of the Regional Science Association, Vol. 13 (), pp. – Google Scholar [6]Cited by: Wilson's () second book (Urban and Regional Models in Geography and Planning) gave a much more complete account and organized the field's technical apparatus.

2 2 The pedagogic treatment of entropy is a theme addressed by Sheppard (), Webber (), Senior (), and Cesario (). Nijkamp and J. Paelinck, A dual interpretation and generalization of entropy maximizing models in regional sciences, Papers Regional Sei.

Assoc. Cited by: 2. ‘‘Entropy Maximization Models in Regional and Urban Planning.’’ International Journal of Mathematical Education in Science and Technology 13(6), – The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information).

Another way of stating this: Take precisely stated prior data or testable information about a probability distribution function.

The entropy model has attached a good deal of attention in transportation analysis, urban and regional planning as well as in other areas. This paper. Entropy in regional analysis.

Quaestiones Geographicae 34(4), Bogucki Wydawnictwo Naukowe, Poznań, pp. 69–78, 5 tables, 5 figs. DOI /quageo, ISSN X. AbstrAct: Entropy has been proposed as a significant tool for an analysis of. implement the maximum entropy models described in [Ratnaparkhi, Reynar and Ratnaparkhi, Ratnaparkhi, ] and also to provide a simple explanation of the maximum entropy formalism.

Disciplines Other Computer Sciences Comments University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCSCited by: Maximum Entropy Models in Science and Engineering 1st Edition.

by J. Kapur (Author) out of 5 stars 2 ratings. ISBN ISBN Why is ISBN important. ISBN. This bar-code number lets you verify that you're getting exactly the right version or edition of a book /5(2).

Book Review: Entropy in Urban and Regional Modelling: by A. WiLsoN. London: Pion. (Monographs in spatial and environmental analysis, No. £250 Show all authorsCited by: 2. Entropy and Information Theory First Edition, Corrected Robert M.

Gray Information Systems Laboratory This book is devoted to the theory of probabilistic information measures and The models are somewhat less general than those of theFile Size: 1MB. O PERATIONS R ESEARCH Vol,No.5,September–October,pp– issnX eissn 08 informs ® doi/opre Chapter 1.

Aoutb Entropy 2 The entropy is mainly de ned with logarithm of base 2, but for technical reasons we will use the natural logarithm.

Note that they di er only by linear factor. Basic characteristics Now we show some basic characteristics of entropy that will be useful in the following chapters. Lemma H(p) 0 for any pFile Size: KB. Entropy Balancing for Causal Effects 27 (MSE) upon a variety of widely used preprocessing adjustments (including Mahalanobis distance match-ing, genetic matching, and matching or weighting on a logistic propensity score).File Size: KB.

In thermodynamics, entropy is commonly associated with the amount of order, disorder, or chaos in a thermodynamic stems from Rudolf Clausius' assertion that any thermodynamic process always "admits to being reduced [reduction] to the alteration in some way or another of the arrangement of the constituent parts of the working body" and that internal.

EMM - Entropy Maximizing Model. Looking for abbreviations of EMM. It is Entropy Maximizing Model. Entropy Maximizing Model listed as EMM. Entropy Maximizing Model; entropy of a partition; entropy of a transformation; entropy of a transformation given a partition; Entropy of activation; Entropy of mixing.

First published inthis groundbreaking investigation into Entropy in Urban and Regional Modelling provides an extensive and detailed insight into the entropy maximising method in the development of a whole class of urban and regional models.

The book has its origins in work being carried out by the author inwhen he realised that the well-known gravity model. Pillai S.U., Shim T.I. () Generalization of the Maximum Entropy Concept and Certain ARMA Processes. In: Mohammad-Djafari A., Demoment G. (eds) Maximum Entropy and Bayesian Methods.

Fundamental Theories of Physics (An International Book Series on The Fundamental Theories of Physics: Their Clarification, Development and Application), vol Author: S. Unnikrishna Pillai, Theodore I.

Shim. Maximum-Entropy Models in Biology, Medicine and Agriculture Maximum-Entropy principle in pharmacokinetics (or in compartment analysis) Maximum-Entropy principle in stochastic prey-predator and epidemic modeis Maximum-Entropy principle in ecological modelling Maximum-Entropy principle in multispecies.

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 Cited by: This Is The First Comprehensive Book About Maximum Entropy Principle And Its Applications To A Diversity Of Fields Like Statistical Mechanics, Thermo-Dynamics, Business, Economics, Insurance, Finance, Contingency Tables, Characterisation Of Probability Distributions (Univariate As Well As Multivariate, Discrete As Well As Continuous), Statistical Inference, Non-Linear.

What is meant by maximum entropy. If we had a fair coin like the one shown below where both heads or tails are equally likely, then we have a case of highest uncertainty in predicting outcome of a toss - this is an example of maximum entropy In co.

$\begingroup$ I am getting quite confused with another statement given in Christopher Bishops book which states that "for a single real variable, the distribution that maximizes the entropy is the Gaussian." It also states that "multivariate distribution with max- imum entropy, for a given covariance, is a Gaussian".

The equivalence of logistic regression and maximum entropy models Nina Zumel recently gave a very clear explanation of logistic regression (The Simpler Derivation of Logistic Regression).

In particular she called out the central role of log-odds ratios and demonstrated how the “deviance” (that mysterious.

After reviewing the laws of thermodynamics, Professor Susskind begins the derivation of how energy states are distributed for a complex system system with. The use of multiple entropy models for Huffman or arithmetic coding is widely used to improve the compression efficiency of many algorithms when the source probability distribution varies.

However, the use of multiple entropy models increases the memory requirements of both the encoder and decoder significantly. In this paper, we present an algorithm which maintains [ ]Author: S. Mehrotra, Wei-ge Chen.

This oft-cited paper explains the concept of Maximum Entropy Models and relates them to natural language processing, specifically as they can be applied to Machine Translation.

Explanation and Discussion Maximum Entropy. The paper goes into a fairly detailed explanation of the motivation behind Maximum Entropy Models. They divide it into 2 sub Category: Paper +. MotivationInformation Entropy Compressing Information MOTIVATION: CASINO I You’re at a casino I You can bet on coins, dice, or roulette I Coins = 2 possible outcomes.

Pays I Dice = 6 possible outcomes. Pays I roulette = 36 possible outcomes. Pays I Suppose you can predict the outcome of a single coin toss/dice roll/roulette spin. I Which would you choose. Contents Prologue xi 1 Information Sources 1 Introduction 1 Probability Spaces and Random Variables.

1 Random. COVID campus closures: see options for getting or retaining Remote Access to subscribed contentCited by: 1. tary properties of the entropy function. In Section 5 we define the primal and dual optimization problems corresponding to the truncated scaling problem and show that the existence of a solution for truncated scaling is equivalent to the attainment of the infimum in the dual optimization problem.Theoretical Computer Science,Pdf.

Berk Kapicioglu, Robert E. Schapire, Martin Wikelski and Tamara Broderick. Combining spatial and telemetric features for learning animal movement models. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, Pdf.2) The entropy of one probability distribution on X relative to another: I(p;q) = X x2X p x ln p x q x is the expected amount of information you gain when you thought the right probability distribution was q and you discover it’s really p.

It can be in nite! There is also category-theoretic characterization of relative entropy.