Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 26th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
American Inst. of Physics, Dec 13, 2006 - Mathematics - 589 pages
The MaxEnt workshops are devoted to Bayesian inference and maximum entropy methods in science and engineering. In addition, this workshop included all aspects of probabilistic inference, such as foundations, techniques, algorithms, and applications. All papers have been peer-reviewed.
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2006 American Institute algorithm analysis applied approximation assume Bayes Bayesian approach Bayesian Inference Bayesian networks calculated coefficients complex components computed conditional conjugate prior consider constraints corresponding defined denotes density detection energy equation estimation exponential exponential family FIGURE flag manifold frequentist function fusion Gaussian geometric given gradient hyper-parameters IEEE Trans independent Independent Component Analysis Inference and Maximum Institute of Physics International Workshop inverse iteration Kullback-Leibler divergence Lagrange multipliers learning likelihood likelihood function linear manifold mapping Markov chain matrix MaxEnt Maximum Entropy Methods mean measure Methods in Science minimizing mixture Mohammad-Djafari noise normalization observed obtained optical flow optimal parameters Phys pixels plasma prior probability distribution problem proposed random Riemannian sampling Science and Engineering segmentation selection sequence simulation solution source separation space spatial spectra spectrum statistical stochastic theorem theory uncertainty updating values variables variance vector