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a 9789401150286

100 
1 

a Erickson, G.

245 
0 
0 
a Maximum Entropy and Bayesian Methods
h Elektronische Ressource
b Boise, Idaho, USA, 1997 Proceedings of the 17th International Workshop on Maximum Entropy and Bayesian Methods of Statistical Analysis
c by G. Erickson, Joshua T. Rychert, C.R. Smith

250 


a 1st ed. 1998

260 


a Dordrecht
b Springer Netherlands
c 1998, 1998

300 


a IX, 302 p
b online resource

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0 

a Massive Inference and Maximum Entropy  CVNP Bayesianism by MCMC  Which Algorithms are Feasible? A Maxent Approach  Maximum Entropy, Likelihood and Uncertainty: A Comparison  Probabilistic Methods for Data Fusion  Whence the Laws of Probability?  Bayesian Group Analysis  SymmetryGroup Justification of Maximum Entropy Method and Generalized Maximum Entropy Methods in Image Processing  Probability Synthesis, How to Express Probabilities in Terms of Each Other  Inversion Based on Computational Simulations  Model Comparison with Energy Confinement Data from Large Fusion Experiments  Deconvolution Based on Experimentally Determined Apparatus Functions  A Bayesian Approach for the Determination of the Charge Density from Elastic Electron Scattering Data  Integrated Deformable Boundary Finding Using Bayesian Strategies  Shape Reconstruction in XRay Tomography from a Small Number of Projections Using Deformable Models  An Empirical Model of Brain Shape  Difficulties Applying Blind Source Separation Techniques to EEG and MEG  The History of Probability Theory  We Must Choose the Simplest Physical Theory: LevinLiVitányi Theorem and Its Potential Physical Applications  Maximum Entropy and Acausal Processes: Astrophysical Applications and Challenges  Computational Exploration of the Entropic Prior Over Spaces of Low Dimensionality  EnvironmentallyOriented Processing of MultiSpectral Satellite Images: New Challenges for Bayesian Methods  Maximum Entropy Approach to Optimal Sensor Placement for Aerospace NonDestructive Testing  Maximum Entropy Under Uncertainty

653 


a Coding and Information Theory

653 


a Coding theory

653 


a Computer science / Mathematics

653 


a Discrete Mathematics in Computer Science

653 


a Statistics

653 


a Artificial Intelligence

653 


a Probability Theory

653 


a Information theory

653 


a Artificial intelligence

653 


a Discrete mathematics

653 


a Statistics

653 


a Probabilities

700 
1 

a Rychert, Joshua T.
e [author]

700 
1 

a Smith, C.R.
e [author]

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0 
7 
a eng
2 ISO 6392

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b SBA
a Springer Book Archives 2004

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0 

a Fundamental Theories of Physics

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a 10.1007/9789401150286

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0 
u https://doi.org/10.1007/9789401150286?nosfx=y
x Verlag
3 Volltext

082 
0 

a 519.2

520 


a This volume has its origin in the Seventeenth International Workshop on Maximum Entropy and Bayesian Methods, MAXENT 97. The workshop was held at Boise State University in Boise, Idaho, on August 4 8, 1997. As in the past, the purpose of the workshop was to bring together researchers in different fields to present papers on applications of Bayesian methods (these include maximum entropy) in science, engineering, medicine, economics, and many other disciplines. Thanks to significant theoretical advances and the personal computer, much progress has been made since our first Workshop in 1981. As indicated by several papers in these proceedings, the subject has matured to a stage in which computational algorithms are the objects of interest, the thrust being on feasibility, efficiency and innovation. Though applications are proliferating at a staggering rate, some in areas that hardly existed a decade ago, it is pleasing that due attention is still being paid to foundations of the subject. The following list of descriptors, applicable to papers in this volume, gives a sense of its contents: deconvolution, inverse problems, instrument (pointspread) function, model comparison, multi sensor data fusion, image processing, tomography, reconstruction, deformable models, pattern recognition, classification and group analysis, segmentation/edge detection, brain shape, marginalization, algorithms, complexity, Ockham's razor as an inference tool, foundations of probability theory, symmetry, history of probability theory and computability. MAXENT 97 and these proceedings could not have been brought to final form without the support and help of a number of people
