Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 1st ed. 2018
MaxEnt 37, Jarinu, Brazil, July 09-14, 2017

Springer Proceedings in Mathematics & Statistics Series, Vol. 239

Coordinators: Polpo Adriano, Stern Julio, Louzada Francisco, Izbicki Rafael, Takada Hellinton

Language: Anglais

179.34 €

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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.

Survival Analysis based on the cumulative hazard function
Anaya-Izquierdo, Karim
University of Bath – UK

A Bayesian hidden Markov mixture model to detect over-expressed chromosome regions
Bambirra, Flávio
Federal University of Minas Gerais – Brazil

Entropic Dynamics: from Entropy and Information Geometry to Quantum Mechanics
Caticha, Ariel
University at Albany (SUNNY) – USA

Data analysis using density gradient
Chen, YenChi
University of Washington – USA

Modelling the kurtosis of spatio-temporal processes
Fonseca, Thais
Federal University of Rio de Janeiro – Brazil

EXONEST: The Bayesian Exoplanetary Explorer
Knuth, Kevin
University at Albany (SUNY) – USA

Quantum Theory is not weird
Skilling, John
Maximum Entropy Data Consultants – Ireland

Uncertainty quantification for complex computer models
Toussaint, Udo
Max-Planck-Institut fuer Plasmaphysik – DE
Presents cutting-edge research from a wide variety of science and engineering fields that use inductive statistics

Examines and discusses the foundations of inductive statistics, addressing the growing difficulty in choosing the optimal method to apply to problems due to the increasing availability of Bayesian methodological alternatives

Expands the available research on Bayesian methods and promotes their application in the scientific community