Bayesian inference and maximum entropy methods in science and engineering: 27th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Saratoga Springs, New York, 8-13 July 2007
For over 25 years the MaxEnt workshops have explored the use of Bayesian probability theory, entropy and information theory in scientific and engineering applications. This volume considers Methods, Applications, and Foundations. Application areas include, but are not limited to: astronomy, physics, chemistry, biology, earth science, and engineering.
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Information and Entropy
Lattice Theory Measures and Probability
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2007 American Institute algorithm analysis applied approach assign Bayes Bayesian Inference Bayesian networks Bayesian probability C. C. Rodriguez calculation Caticha coefficients compute constraints correlation corresponding covariance matrix data likelihood defined derived E. T. Jaynes edited by K. H. emission entropic prior equation error estimation example experimental Figure filter function Gaussian Giffin given IEEE Inference and Maximum Information Geometry integration interactions International Workshop inverse Jaynes joint K. H. Knuth lattice likelihood function linear logic machine learning Markov MaxEnt maximizes Maximum Entropy Methods MCMC measurement Methods in Science noise normal observed obtained parameters physical pixel points posterior probability prior information prior probability probabilistic probability density probability distribution probability theory problem propagation quantum reconstruction Rodriguez O 2007 rule samples Science and Engineering signal simulated space spectra statistical statistical syllogism theorem transformation uncertainties updating values variables variance vector Workshop on Bayesian