Show simple item record

MaxEnt 2019—Proceedings, 2019, MaxEnt 2019The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

dc.contributor.authorVon Toussaint, Udo*
dc.contributor.authorPreuss, Roland*
dc.date.accessioned2021-02-11T18:57:25Z
dc.date.available2021-02-11T18:57:25Z
dc.date.issued2020*
dc.date.submitted2020-04-07 23:07:09*
dc.identifier44842*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/52908
dc.description.abstractThis Proceedings book presents papers from the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2019. The workshop took place at the Max Planck Institute for Plasma Physics in Garching near Munich, Germany, from 30 June to 5 July 2019, and invited contributions on all aspects of probabilistic inference, including novel techniques, applications, and work that sheds new light on the foundations of inference. Addressed are inverse and uncertainty quantification (UQ) and problems arising from a large variety of applications, such as earth science, astrophysics, material and plasma science, imaging in geophysics and medicine, nondestructive testing, density estimation, remote sensing, Gaussian process (GP) regression, optimal experimental design, data assimilation, and data mining.*
dc.languageEnglish*
dc.subjectQA1-939*
dc.subjectQ1-390*
dc.subject.classificationbic Book Industry Communication::P Mathematics & scienceen_US
dc.subject.otheruncertainty quantification*
dc.subject.otherorthodontics*
dc.subject.otherevidence*
dc.subject.otherglobal statistical regularization*
dc.subject.otherMCMC*
dc.subject.otherfield reconstruction*
dc.subject.othermeshless methods*
dc.subject.otherannealed importance sampling*
dc.subject.othercervical vertebra maturation*
dc.subject.otherBayesian evidence*
dc.subject.otherspectral expansion*
dc.subject.othernon-intrusive*
dc.subject.othermodel comparison*
dc.subject.otherplasma-wall interactions*
dc.subject.othernested sampling*
dc.subject.otherDeep Learning (DL)*
dc.subject.otherclassification*
dc.subject.otherstochastic gradients*
dc.subject.otherBayesian Maximum a Posteriori approach*
dc.subject.otherConvolutional Neural Network (CNN)*
dc.subject.otherimpedance cardiography*
dc.subject.othervowel*
dc.subject.otherSGHMC*
dc.subject.otherGaussian process regression*
dc.subject.otherprecise hypotheses*
dc.subject.otherformant*
dc.subject.otherBayesian analysis*
dc.subject.otherthermodynamic Integration*
dc.subject.othermodel averaging*
dc.subject.otherprobability theory*
dc.subject.otheracoustic phonetics*
dc.subject.otherUAP*
dc.subject.otherentropy prior probability*
dc.subject.othersource localization*
dc.subject.otherUAV*
dc.subject.othersource-filter theory*
dc.subject.otherSPECT*
dc.subject.othermulti fidelity*
dc.subject.otherArtificial Intelligence (AI)*
dc.subject.otherMonte Carlo*
dc.subject.otherTic-Tac*
dc.subject.otherpragmatic hypotheses*
dc.subject.othercluster analysis*
dc.subject.otheraortic dissection*
dc.subject.otherphysics-informed methods*
dc.subject.otherUFO*
dc.subject.otherHMC*
dc.subject.othersteady-state*
dc.subject.othermean shift method*
dc.subject.otherBayes*
dc.subject.otherNimitz*
dc.subject.otherimage reconstruction*
dc.subject.othermachine learning*
dc.subject.otherlocal statistical regularization*
dc.subject.othermarginal likelihood*
dc.subject.otherdetrending*
dc.subject.otherGaussian processes*
dc.subject.otherkernel methods*
dc.subject.otherpartial differential equations*
dc.subject.otherhypothesis tests*
dc.subject.otherPET*
dc.titleMaxEnt 2019—Proceedings, 2019, MaxEnt 2019The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03928-477-1*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
oapen.relation.isbn9783039284771*
oapen.relation.isbn9783039284764*
oapen.pages312*
oapen.edition1st*


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

https://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/