Bayesian Inference and Maximum Entropy Methods in Science and Engineering

MaxEnt 37, Jarinu, Brazil, July 09–14, 2017

Nonfiction, Science & Nature, Science, Physics, Thermodynamics, Mathematics, Statistics
Cover of the book Bayesian Inference and Maximum Entropy Methods in Science and Engineering by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783319911434
Publisher: Springer International Publishing Publication: July 12, 2018
Imprint: Springer Language: English
Author:
ISBN: 9783319911434
Publisher: Springer International Publishing
Publication: July 12, 2018
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Manipulation of Multiphase Materials for Touch-less Nanobiotechnology by
Cover of the book Formation, Evolution, and Dynamics of Young Solar Systems by
Cover of the book Dracula by
Cover of the book Novel Methods and Technologies for Enterprise Information Systems by
Cover of the book Bipolar Depression: Molecular Neurobiology, Clinical Diagnosis, and Pharmacotherapy by
Cover of the book Learning Portuguese as a Second Language by
Cover of the book Dynamics and Control of Advanced Structures and Machines by
Cover of the book Resisting Violence by
Cover of the book Advances in Ergonomics in Design by
Cover of the book Artificial Neural Networks in Pattern Recognition by
Cover of the book Surgery for Chest Wall Deformities by
Cover of the book The Arab Uprisings in Egypt, Jordan and Tunisia by
Cover of the book Demography, Politics, and Partisan Polarization in the United States, 1828–2016 by
Cover of the book Sensors for Everyday Life by
Cover of the book Type IV Secretion in Gram-Negative and Gram-Positive Bacteria by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy