Titlebook: Maximum Entropy and Bayesian Methods; Seattle, 1991 C. Ray Smith,Gary J. Erickson,Paul O. Neudorfer Book 1992 Springer Science+Business Med

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Unmixing Mineral Spectra Using a Neural Net with Maximum Entropy Regularization,ions of a set of reference spectra. To do this, we train a neural net, the distributed associative memory [DAM], to remember the reference spectra. If the reference spectra are linearly independent, and in particular, if the number of features characterizing the spectra is greater than the number of

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Bayesian Mixture Modeling,bits the true Bayesian predictive distribution, implicitly integrating over the entire underlying parameter space. An infinite number of mixture components can be accommodated without difficulty, using a prior distribution for mixing proportions that selects a reasonable subset of components to expl

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