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Bayesian Classification Using an Entropy Prior on Mixture Models
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2001 American Institute aggregatible algorithm aliases amplitude approximation assume attribute vector background bandwidth Bayes factor Bayesian Inference Bayesian Methods Boolean algebra causal Chain Monte Carlo changepoints cluster compute constraints data points data set data values decay rate constant defined density estimation derivatives discrete Fourier transform edited by J. T. entropy prior equations errors example experiment exponential factor FIGURE finite frequency estimation gadoteridol gage Gaussian given Hessian images Independent Component Analysis Inference and Maximum integral inverse kernel knot positions likelihood linear Markov Chain Markov Chain Monte matrix Maxentropic reconstruction Maximum Entropy Methods measure mixture model model parameters noise nonsimultaneously sampled data nonuniformly nonsimultaneously sampled Nyquist critical frequency obtain parameter estimates partition lattices peak periodogram physical pixel Poisson posterior probability power spectrum probability distribution probability theory problem QDOE sensor shown in Fig signal simulated data snubber solution source separation space stationary sinusoid subtypes sufficient statistic target theorem trawls uncertainties zero