Maximum-entropy Models in Science and Engineering
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 Time Series, Pattern Recognition, Marketing And Elections, Operations Research And Reliability Theory, Image Processing, Computerised Tomography, Biology And Medicine. There Are Over 600 Specially Constructed Exercises And Extensive Historical And Bibliographical Notes At The End Of Each Chapter.The Book Should Be Of Interest To All Applied Mathematicians, Physicists, Statisticians, Economists, Engineers Of All Types, Business Scientists, Life Scientists, Medical Scientists, Radiologists And Operations Researchers Who Are Interested In Applying The Powerful Methodology Based On Maximum Entropy Principle In Their Respective Fields.
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MaximumEntropy Discrete Univariate Probability Distributions
MaximumEntropy Continuous Univariate Probability
MaximumEntropy Discrete Multivariate Probability
MaximumEntropy Continuous Multivariate Probability
MaximumEntropy Distributions in Statistical Mechanics
Minimum Discrepancy Measures
Concavity Convexity of MaximumEntropy Minimum
MaximumEntropy Models in Regional and Urban Planning
MaximumEntropy Models in Marketing and Elections
MaximumEntropy Models in Economics Finance Insurance
MaximumEntropy Spectral Analysis
17 MaximumEntropy Image Reconstruction
Maximum and MinimumEntropy Models in Pattern
MaximumEntropy Principle in Operations Research
MaximumEntropy Models in Biology Medicine
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algorithm beta distribution Bose-Einstein Bose-Einstein distribution brands bution Cauchy distribution concave function constraints contingency table convex function corresponding cost covariances decreasing determined directed divergence Dirichlet distribution discussed distri entropy maximization entropy principle equal equations estimate expected number expected value exponential Fermi-Dirac gamma distribution Gaussian generalised given gives i-i i-i increasing function interval Kapur Kullback Lagrange's method Lagrangian Laplace distribution linear market shares matrix maximize the entropy maximum likelihood maximum value maximum-entropy distribution maximum-entropy principle maximum-entropy probability distribution measure of entropy MEPD minimize multivariate distribution multivariate normal distribution Negative Binomial Distribution normal distribution number of particles obtained parameters Poisson distribution population prescribed prior probability probability density function problem Renyi's satisfied Shannon's measure Show solution solve statistical stochastic model tion variates vector zero
Page 605 - On the best finite set of linear observables for discriminating two Gaussian signals.