Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 20th International Workshop, Gif-sur-Yvette, France, 8-13 July 2000
American Inst. of Physics, Jun 8, 2001 - Mathematics - 644 pages
Bayesian inference and maximum entropy methods are central points of new scientific inference in mathematical physics and in all inverse problems in engineering and all probabilistic data analysis. This volume contains peer-reviewed selection of the papers presented at this international workshop. Topics included are: axiomatics and concepts, bayesian parameter estimation, algorithms for bayesian computation, deconvolution and source separation, quantum tomography, tomographic imaging and image processing, as well as bayesian inference in applications.
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chaired by A J M Garrett
chaired by C Bendjaballah M H Partovi and A Plastino
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2001 American Institute algorithm analysis applications approximation Bayes Bayesian Inference Bayesian Methods binary coefficients complex computed consider constraints corresponding defined denotes density density matrix derived detector discrete dynamical energy Entropy and Bayesian equation error estimation example FIGURE finite frequency Gaussian geometry given hyperparameters independent Inference and Maximum Institute of Physics integral International Workshop iterations Jaynes likelihood function linear logical MAP estimate Markov MaxEnt maximization maximum a posteriori Maximum Entropy Methods maximum likelihood measure Methods in Science minimization mixing matrix mixture model Mohammad-Djafari noise nonlinear normal observed obtained optimal parameters periodogram photons Phys pixel posterior probability probability distribution quantum question random reconstruction reference function robot sampling Science and Engineering segment sensor sequence Shannon Shannon entropy simulation solution source separation source signals space statistical support vector machine Theorem theory transform uncertainty variables variance vector voxels