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Titlebook: Maximum Entropy and Bayesian Methods; Boise, Idaho, USA, 1 Gary J. Erickson,Joshua T. Rychert,C. Ray Smith Conference proceedings 1998 Spri

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書目名稱Maximum Entropy and Bayesian Methods
副標題Boise, Idaho, USA, 1
編輯Gary J. Erickson,Joshua T. Rychert,C. Ray Smith
視頻videohttp://file.papertrans.cn/628/627909/627909.mp4
叢書名稱Fundamental Theories of Physics
圖書封面Titlebook: Maximum Entropy and Bayesian Methods; Boise, Idaho, USA, 1 Gary J. Erickson,Joshua T. Rychert,C. Ray Smith Conference proceedings 1998 Spri
描述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 (point-spread) function, model comparison, multi se
出版日期Conference proceedings 1998
關(guān)鍵詞Experiment; Maximum entropy method; Potential; Probability theory; algorithms; bayesian statistics; best f
版次1
doihttps://doi.org/10.1007/978-94-011-5028-6
isbn_softcover978-94-010-6111-7
isbn_ebook978-94-011-5028-6Series ISSN 0168-1222 Series E-ISSN 2365-6425
issn_series 0168-1222
copyrightSpringer Science+Business Media Dordrecht 1998
The information of publication is updating

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An Empirical Model of Brain Shape,rence configuration. The resultant representation ensures parsimony, yet captures information about the variation across the entire volumetric extent of the brain samples, and facilitates analyses that are governed by the measured statistics of anatomic variability rather than by the physics of some assumed model.
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Massive Inference and Maximum Entropy,ignment. We call this technique “Massive Inference” (MassInf). Although the entropy formula no longer appears in the prior, MassInf results show improved quality. MassInf is also capable of assigning a simple prior for polarized images.
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Whence the Laws of Probability?,nctional equation is then set up for the relation between the probabilities and is solved. By synthesising the non-primitive operations NOT and AND from NAND the sum and product rules are derived from this one formula, the fundamental ‘law of probability’.
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