Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and EngineeringKevin H. Knuth 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. |
Contents
Bayesics | 3 |
Maximum Entropy Distributions Between Upper and Lower Bounds | 25 |
Information Theoretical Approach for Searching Very Large | 43 |
Copyright | |
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Common terms and phrases
2005 American Institute Abbas algorithm analysis applications approximation assume Bayes factors Bayesian Inference Bayesian network bounds cluster components compute conditional conditional independence constraints data set defined density derived edited by Kevin Engineering edited equation error estimate example FIGURE Fisher information frequency Gaussian geometry Gibbs sampling given independent Inference and Maximum Institute of Physics integral inverse problem iteration Jaynes Kevin H Knuth labels likelihood function linear marginal marginal likelihood Markov matrix MaxEnt Maximum Entropy Methods MCMC measure Methods in Science mixture model model selection Monte Carlo nodes noise normal observed obtained optimal P₁ parameters Particle Filter partition Patrick Castle pixels plausible posterior probability principle prior distribution prior probability probability distribution random variable reconstruction sampling Science and Engineering signal simulated solution sources space statistical structure theorem theory tree uncertainty uniform vector Zeff