Maximum Entropy and Bayesian Methods: Fundamental Theories of Physics, cartea 70
Editat de John Skilling, Sibusio Sibisien Limba Engleză Paperback – 20 sep 2011
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Specificații
ISBN-13: 9789401065344
ISBN-10: 9401065349
Pagini: 340
Ilustrații: XI, 323 p.
Dimensiuni: 160 x 240 x 19 mm
Greutate: 0.55 kg
Ediția:Softcover reprint of the original 1st ed. 1996
Editura: Springer
Colecția Fundamental Theories of Physics
Seria Fundamental Theories of Physics
Locul publicării:Dordrecht, Netherlands
ISBN-10: 9401065349
Pagini: 340
Ilustrații: XI, 323 p.
Dimensiuni: 160 x 240 x 19 mm
Greutate: 0.55 kg
Ediția:Softcover reprint of the original 1st ed. 1996
Editura: Springer
Colecția Fundamental Theories of Physics
Seria Fundamental Theories of Physics
Locul publicării:Dordrecht, Netherlands
Public țintă
ResearchCuprins
Applications.- Flow and diffusion images from Bayesian spectral analysis of motion-encoded NMR data.- Bayesian estimation of MR images from incomplete raw data.- Quantified maximum entropy and biological EPR spectra.- The vital importance of prior information for the decomposition of ion scattering spectroscopy data.- Bayesian consideration of the tomography problem.- Using MaxEnt to determine nuclear level densities.- A fresh look at model selection in inverse scattering.- The maximum entropy method in small-angle scattering.- Maximum entropy multi-resolution EM tomography by adaptive subdivision.- High resolution image construction from IRAS survey — parallelization and artifact suppression.- Maximum entropy performance analysis of spread-spectrum multiple-access communications.- Noise analysis in optical fibre sensing: A study using the maximum entropy method.- Algorithms.- AutoClass — a Bayesian approach to classification.- Evolution reviews of BayesCalc, a MATHEMATICA package for doing Bayesian calculations.- Bayesian inference for basis function selection in nonlinear system identification using genetic algorithms.- The meaning of the word “Probability”.- The hard truth.- Are the samples doped — If so, how much?.- Confidence intervals from one observation.- Hypothesis refinement.- Bayesian density estimation.- Scale-invariant Markov models for Bayesian inversion of linear inverse problems.- Foundations: Indifference, independence and MaxEnt.- The maximum entropy on the mean method, noise and sensitivity.- The maximum entropy algorithm applied to the two-dimensional random packing problem.- Neural Networks.- Bayesian comparison of models for images.- Interpolation models with multiple hyperparameters.- Density networks and their application to proteinmodelling.- The cluster expansion: A hierarchical density model.- The partitioned mixture distribution: Multiple overlapping density models.- Physics.- Generating functional for the BBGKY hierarchy and the N-identical-body problem.- Entropies for continua: Fluids and magnetofluids.- A logical foundation for real thermodynamics.