Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Kevin H. Knuth, Ali E. Abbas, Robin D. Morris, J. Patrick Castle
American Inst. of Physics, Dec 6, 2005 - Mathematics - 564 pages
All papers were peer-reviewed. For over 25 years the MaxEnt workshops have explored Bayesian and Maximum Entropy methods in scientific, engineering, and signal processing applications. This proceedings volume covers all aspects of probabilistic inference such as techniques, applications, and foundations. Applications include physics, space science, earth science, biology, imaging, graphical models and source separation.
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Maximum Entropy Distributions Between Upper and Lower Bounds
Information Theoretical Approach for Searching Very Large
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2005 American Institute Abbas algorithm analysis applications approximation assume Bayes factors Bayesian Inference Bayesian network bounds cluster coefficients components compute conditional conditional independence consider constraints corresponding data set defined density derived determine edited by Kevin Engineering edited equation error estimate evidence example exponential FIGURE Fisher information frequency Gaussian geometry Gibbs sampling given hypotheses independent Inference and Maximum Institute of Physics integral inverse problem iteration Jaynes Kevin H Knuth labels likelihood function linear marginal marginal likelihood MaxEnt Maximum Entropy Methods MCMC mean measure Methods in Science mixture model model selection Monte Carlo nodes noise normal observed obtained parameters Particle Filter Patrick Castle pixels plausible posterior probability principle prior distribution prior probability probability distribution random variable reconstruction sampling Science and Engineering shows signal simulated solution sources space statistical structure theorem theory tree uncertainty uniform vector Zeff zero