Applied Information Systems Research Program (AISRP). Workshop 2: Meeting Proceedings PDF Download
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Languages : en
Pages : 280
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Author:
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Category :
Languages : en
Pages : 280
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Author:
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Category : Aeronautics
Languages : en
Pages : 380
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Book Description
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
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Category : Government publications
Languages : en
Pages : 1168
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Category : Government publications
Languages : en
Pages : 1452
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Category : Government publications
Languages : en
Pages : 1208
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Category : Library science
Languages : en
Pages : 732
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Category : Science
Languages : en
Pages : 1428
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Category : Natural resources
Languages : en
Pages : 446
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Author: Joseph M. Hilbe
Publisher: Cambridge University Press
ISBN: 1108210740
Category : Mathematics
Languages : en
Pages : 429
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Book Description
This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.
Author: Lutz H. Hamel
Publisher: John Wiley & Sons
ISBN: 1118211030
Category : Computers
Languages : en
Pages : 211
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Book Description
An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
