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Bayesian Inference and Maximum Entropy Methods in Science and Engineering: MaxEnt 37, Jarinu, Brazil, July 09–14, 2017: 239 (Springer Proceedings in Mathematics & Statistics, 239) - Softcover
- PublisherSpringer
- Publication date2019
- ISBN 10 3030081869
- ISBN 13 9783030081867
- BindingPaperback
- LanguageEnglish
- Number of pages320
- EditorPolpo Adriano, Stern Julio, Louzada Francisco, Izbicki Rafael, Takada Hellinton
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering: MaxEnt 37, Jarinu, Brazil, July 09-14, 2017 (Springer Proceedings in Mathematics & Statistics, 239)
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering: MaxEnt 37, Jarinu, Brazil, July 09-14, 2017 (Springer Proceedings in Mathematics & Statistics, 239)
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering
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Springer International Publishing, 2019
ISBN 10: 3030081869
ISBN 13: 9783030081867
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Adriano Polpo, Ph.D., is an Associate Professor of Statistics at the Federal University of Sao Carlos (UFSCar, Brazil). He received his B.Sc. in Statistics from the State University of Campinas, his Ph.D. in Statistics from the University of . Seller Inventory # 448671268
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering
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Springer International Publishing Jan 2019, 2019
ISBN 10: 3030081869
ISBN 13: 9783030081867
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in São Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their application in the scientific community. They gather research from scholars in many different fields who use inductive statistics methods and focus on the foundations of the Bayesian paradigm, their comparison to objectivistic or frequentist statistics counterparts, and their appropriate applications.Interest in the foundations of inductive statistics has been growing with the increasing availability of Bayesian methodological alternatives, and scientists now face much more difficult choices in finding the optimal methods to apply to their problems. By carefully examining and discussing the relevant foundations, the scientific community can avoid applying Bayesian methods on a merely ad hoc basis.For over 35 years, the MaxEnt workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering application contexts. The workshops welcome contributions on all aspects of probabilistic inference, including novel techniques and applications, and work that sheds new light on the foundations of inference. Areas of application in these workshops include astronomy and astrophysics, chemistry, communications theory, cosmology, climate studies, earth science, fluid mechanics, genetics, geophysics, machine learning, materials science, medical imaging, nanoscience, source separation, thermodynamics (equilibrium and non-equilibrium), particle physics, plasma physics, quantum mechanics, robotics, and the social sciences. Bayesian computational techniques such as Markov chain Monte Carlo sampling are also regular topics, as are approximate inferential methods. Foundational issues involving probability theory and information theory, as well as novel applications of inference to illuminate the foundations of physical theories, are also of keen interest. 320 pp. Englisch. Seller Inventory # 9783030081867
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering : MaxEnt 37, Jarinu, Brazil, July 09¿14, 2017
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ISBN 10: 3030081869
ISBN 13: 9783030081867
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in São Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their application in the scientific community. They gather research from scholars in many different fields who use inductive statistics methods and focus on the foundations of the Bayesian paradigm, their comparison to objectivistic or frequentist statistics counterparts, and their appropriate applications.Interest in the foundations of inductive statistics has been growing with the increasing availability of Bayesian methodological alternatives, and scientists now face much more difficult choices in finding the optimal methods to apply to their problems. By carefully examining and discussing the relevant foundations, the scientific community can avoid applying Bayesian methods on a merely ad hoc basis.For over 35 years, the MaxEnt workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering application contexts. The workshops welcome contributions on all aspects of probabilistic inference, including novel techniques and applications, and work that sheds new light on the foundations of inference. Areas of application in these workshops include astronomy and astrophysics, chemistry, communications theory, cosmology, climate studies, earth science, fluid mechanics, genetics, geophysics, machine learning, materials science, medical imaging, nanoscience, source separation, thermodynamics (equilibrium and non-equilibrium), particle physics, plasma physics, quantum mechanics, robotics, and the social sciences. Bayesian computational techniques such as Markov chain Monte Carlo sampling are also regular topics, as are approximate inferential methods. Foundational issues involving probability theory and information theory, as well as novel applications of inferenceto illuminate the foundations of physical theories, are also of keen interest. Seller Inventory # 9783030081867
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering: MaxEnt 37, Jarinu, Brazil, July 09-14, 2017 (Springer Proceedings in Mathematics & Statistics, 239)
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Bayesian Inference and Maximum Entropy Methods in Science and Engineering : MaxEnt 37, Jarinu, Brazil, July 09?14, 2017
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