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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Bayesian Interpolation,ian approach to regularisation and model-comparison is demonstrated by studying the inference problem of interpolating noisy data. The concepts and methods described are quite general and can be applied to many other data modelling problems. Regularising constants are set by examining their posterio

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A Bayesian Method for the Detection of a Periodic Signal of Unknown Shape and Period,a signal or of its characteristics. It is applicable to data consisting of the locations or times of individual events. To address the detection problem, we use Bayes’ theorem to compare a constant rate model for the signal to models with periodic structure. The periodic models describe the signal p

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Linking the Plausible and Demonstrative Inferences, been related to demonstrative patterns of classic logic. The connection between all the plausible inference patterns and the demonstrative patterns is examined in a sentential plausible logic language. . commonsense approach is presented as a formalized system from only two elementary monotonicity

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Maximum Likelihood Estimation of the Lagrange Parameters of the Maximum Entropy Distributions,} of known functions ..(.), 0,..., .. The solution depends on . + 1 Lagrange multipliers which are determined by solving the set of nonlinear equations formed by the N data constraints and the normalization constraint. The problem we address here is different. It consists of estimating these Lagrang

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