5 edition of Maximum Entropy found in the catalog.
February 14, 2001
by John Wiley & Sons Inc
Written in English
|The Physical Object|
|Number of Pages||324|
This text/reference presents practical maximum-entropy models used in both the hard and the soft sciences. The basic theory of the maximum-entropy principle is explained, followed by detailed applications in numerous areas of science, business and economics, including thermodynamics, mechanics, statistics, design of experiments, time-series analysis, queuing and decision t/5(4). Abstract. Maximum entropy and minimum cross-entropy estimation are applica-ble when faced with ill-posed estimation problems. I introduce a Stata command that estimates a probability distribution using a maximum entropy or minimum cross-entropy criterion. I show how this command can be used to calibrate survey data to various population totals.
Book:Entropy. Jump to navigation Jump to search. This page is currently inactive Approximate entropy Maximum Entropy Principle of maximum entropy Maximum entropy probability distribution Maximum-entropy Markov model Markov chain Markov model Markov information source. MAXENT (MAXimum ENTropy principle) is a general method of statistical inference derived from and intrinsic to statistical mechanics. The probabilities it produces are “logical probabilities” – measures of the logical relationship between hypothesis and evidence.
Summary: “Entropy” “Entropy” is a short story by Thomas Pynchon. It is a part of his collection Slow Learner, and was originally published in the Kenyon Review in , while Pynchon was still an his introduction to the collection, Pynchon refers to “Entropy” as the work of a “beginning writer” (12). maximum entropy yields 'a uniform distribution' (e.g. density and composition in room air), but most often not (e.g. density and composition in a gravity field). • By the way, entropy is the reason why oxygen in the air, which is heavier than nitrogen, does not.
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The Maximum Entropy Method addresses the principle and applications of the powerful maximum entropy method (MEM), which has its roots in the principle of maximum entropy introduced into the field of statistical mechanics almost 40 years ago.
This method has since been adopted in many areas of science and technology, such as spectral analysis, image restoration, mathematics, and : $ 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 Cited by: 5.
Maximum Entropy Formalism brings together sixteen papers that grew out of a conference held at MIT in May The range and depth of the contributions will make the book useful to an unusually large audience.
Chemists, biologists, ecologists, systems engineers and modelers, physicists, and social scientists will find here a comprehensive 5/5(2). This book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy.
In statistics and information theory, a maximum entropy probability distribution has entropy that is at least as great as that of all other members of a specified class of probability ing to the principle of maximum entropy, if nothing is known about a distribution except that it belongs to a certain class (usually defined in terms of specified properties or measures).
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.
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 Spectral Analysis Of.
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. the maximum entropy) consistent with your constraints.
That way you have not introduced any additional assumptions or biases into your calculations. This principle was used in the last chapter in the simple case of three probabilities and one constraint. The entropy could be maximized analytically.
Using the constraint and the fact that the. The experiments demonstrate that: 1) the Tsallis entropy is superior to traditional maximum entropy thresholding, maximum between class variance thresholding, and minimum cross entropy thresholding; 2) the artificial bee colony is more rapid than either genetic algorithm or particle swarm optimization.
Therefore, our approach is effective and. The Twelfth International Workshop on Maximum Entropy and Bayesian Methods in Sciences and Engineering (MaxEnt 92) was held in Paris, France, at the Centre National de la Recherche Scientifique (CNRS), July The application of a maximum entropy criterion for solving the spectral extrapolation problem is usually attributed to a research thesis by J P Burg published in at Stanford University, USA.
This technique is sometimes called the ‘all poles’ method because of the nature of the estimation model used. It is a method which is often associated with the reconstruction of a power spectrum.
Maximum Entropy and Ecology would work well as a textbook for a graduate course in theoretical ecology and macroecology, with an emphasis on application of maximum entropy." -- The Quarterly Review of Biology.
About the Author. John Harte is a Professor of Ecosystem Sciences at the University of California, Berkeley. Following undergraduate Cited by: The operational regimes at maximum thermal efficiency, maximum power output, and minimum entropy production rate are compared for each of these engines.
It will be shown that in an endoreversible heat engine, a reduction in the entropy production corresponds to an increase in thermal efficiency. Entropy in ergodic theory and homogeneous dynamics Manfred Einsiedler, Elon Lindenstrauss, and Thomas Ward This is the second volume of a project that began with the volume Ergodic Theory with a view toward Number Theory by Einsiedler and Ward.
Maximum Entropy Econometrics will be of interest to econometricianstrying to devise procedures for recovering information from partialor incomplete data, as well as quantitative economists in financeand business, statisticians, and students and applied researchersin econometrics, engineering and the physical by: Harry F.
Martz, Ray A. Waller, in Methods in Experimental Physics, Neutron Scattering. The modem maximum entropy method [9–12] is an optimal Bayesian method that is appropriate for making inferences about positive and additive ly, it has been used to analyze neutron scattering data [ 13–15], which we now illustrate.
The Maximum Entropy Principle also eliminates the mystery as to the origin of the mathematical expressions underlying all probability distributions. The MEP derivation for the Gaussian and generalized Cauchy distributions is shown in detail. The MEP is also related to Fisher information and the Kullback-Leibler measure of relative entropy.5/5(2).
In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent in the variable's possible outcomes.
The concept of information entropy was introduced by Claude Shannon in his paper "A Mathematical Theory of Communication".
As an example, consider a biased coin with probability p of landing on heads and probability 1-p. Chieu H and Ng H A maximum entropy approach to information extraction from semi-structured and free text Eighteenth national conference on Artificial intelligence, () Amaya F and Benedí J Improvement of a Whole Sentence Maximum Entropy Language Model using grammatical features Proceedings of the 39th Annual Meeting on Association for.
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,and from August 9 to 11, Maximum Entropy - Kindle edition by Luzzatto, Kfir.
Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Maximum : Kfir Luzzatto.Maximum Entropy Distributions Uniform Distribution Among probability distributions which are nonzero over a finite range of values, the maximum-entropy distribution is the uniform.
distribution. To show this, we must maximize the entropy.