Titlebook: Maximum Entropy and Bayesian Methods; Paul F. Fougère Book 1990 Kluwer Academic Publishers 1990 Maximum entropy method.Probability theory.

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Consistency Principle for Data-Based Probabilistic Inference,iance under conditioning of the random variable domain is stated. Such a principle is formalized using the paradigm of a two-step inference method based on data. Invariance requirements are expressed both in the data encoding and probability distribution selection steps..Distributions may be assigne

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刺耳

Atmospheric 14C Variations: A Bayesian Prospect,periodogram, and maximum entropy or MEM), the spectrum is characterized by features at ~ 2200, 900, 700, 207, 149, and 88 years as well possibly at other frequencies. Model fitting of sinusoids using the Bretthorst algorithm confirms most of these and places their periods on a more secure basis. The

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On Decoupling Probability from Kinematics in Quantum Mechanics,rms of Spacetime Algebra. The reformulation admits a separation of the Dirac wave function into a two parameter probability factor and a six parameter kinematical factor. The complex valuedness of the wave function as well as its bilinearity in observables have perfect kinematical interpretations in

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Bayesian Model Selection and Parameter Estimation Applied to Sea Floor Pressure Data, also allows the removal of parameters, which are of no interest, from the models, thus concentrating attention on only those parameters which are of interest. Artificial time series are analyzed with the algorithm, which is shown to be robust in the presence of noise, and the results are compared w

CRUDE

Applications of Maximum Entropy and Bayesian Methods in Neutron Scattering,ough the first applications were straight-forward deconvolutions, the work has been extended to make routine use of multi-channel entropy to additionally determine (broad) unknown backgrounds. A more exotic example of the use of MaxEnt involves the study of aggregation in a biological sample using F

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Solving Oversampled Data Problems By Maximum Entropy,t the amount of independent information they contain is very much less than the actual number of data points. Examples of problems for which this algorithm is particularly appropriate are dynamic light scattering, solution scattering and fibre diffraction. The application of a general purpose entrop

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Constructing Priors in Maximum Entropy Methods,d sum of the conceivable priors with weights that depend exponentially on the overlap of the prior with the exponential part of the maximum entropy probability. With additional information, one can iteratively improve the prior and sharpen the choice between alternative priors. Our construction can