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

種類:

電子ブック

責任表示:

edited by Adriano Polpo, Julio Stern, Francisco Louzada, Rafael Izbicki, Hellinton Takada

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2018

著者名:

Polpo, Adriano.

Stern, Julio.

Louzada, Francisco.

Izbicki, Rafael.

Takada, Hellinton.

SpringerLink (Online service)

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シリーズ名:

Springer Proceedings in Mathematics & Statistics ; 239

ISBN:

9783319911434 [3319911430]    

注記:

Ariel Caticha,Quantum phases in entropic dynamics -- Ali Mohammad-Djafari,Bayesian Approach to Variable Splitting - Link with ADMM Methods -- Afonso Vaz,Prior shift using the Ratio Estimator -- Camila B. Martins,Bayesian meta-analytic measure -- Diego Marcondes,Feature Selection from Local Lift Dependence based Partitions -- Dirk Nille,Probabilistic Inference of Surface Heat Flux Densities from Infrared Thermography -- Donald Spector,Schrödinger's Zebra: Applying Mutual Information Maximization to Graphical Halftoning -- Geert Verdoolaege,Regression of Fluctuating System Properties: Baryonic Tully-Fisher Scaling in Disk Galaxies -- Hellinton Takada,Bayesian Portfolio Optimization for Electricity Generation Planning -- Jony Pinto Junior,Bayesian variable selection methods for log-Gaussian Cox processes -- Keith Earle,Effect of Hindered Diffusion on the Parameter Sensitivity of Magnetic Resonance Spectra -- Leandro Ferreira,The random Bernstein polynomial smoothing via ABC method -- Nestor Caticha,Mean Field st
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

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