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

Von Toussaint, Udo; Preuss, Roland

doi:10.3390/books978-3-03928-477-1

dc.contributor.author	Von Toussaint, Udo	*
dc.contributor.author	Preuss, Roland	*
dc.date.accessioned	2021-02-11T18:57:25Z
dc.date.available	2021-02-11T18:57:25Z
dc.date.issued	2020	*
dc.date.submitted	2020-04-07 23:07:09	*
dc.identifier	44842	*
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/52908
dc.description.abstract	This 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.language	English	*
dc.subject	QA1-939	*
dc.subject	Q1-390	*
dc.subject.classification	bic Book Industry Communication::P Mathematics & science	en_US
dc.subject.other	uncertainty quantification	*
dc.subject.other	orthodontics	*
dc.subject.other	evidence	*
dc.subject.other	global statistical regularization	*
dc.subject.other	MCMC	*
dc.subject.other	field reconstruction	*
dc.subject.other	meshless methods	*
dc.subject.other	annealed importance sampling	*
dc.subject.other	cervical vertebra maturation	*
dc.subject.other	Bayesian evidence	*
dc.subject.other	spectral expansion	*
dc.subject.other	non-intrusive	*
dc.subject.other	model comparison	*
dc.subject.other	plasma-wall interactions	*
dc.subject.other	nested sampling	*
dc.subject.other	Deep Learning (DL)	*
dc.subject.other	classification	*
dc.subject.other	stochastic gradients	*
dc.subject.other	Bayesian Maximum a Posteriori approach	*
dc.subject.other	Convolutional Neural Network (CNN)	*
dc.subject.other	impedance cardiography	*
dc.subject.other	vowel	*
dc.subject.other	SGHMC	*
dc.subject.other	Gaussian process regression	*
dc.subject.other	precise hypotheses	*
dc.subject.other	formant	*
dc.subject.other	Bayesian analysis	*
dc.subject.other	thermodynamic Integration	*
dc.subject.other	model averaging	*
dc.subject.other	probability theory	*
dc.subject.other	acoustic phonetics	*
dc.subject.other	UAP	*
dc.subject.other	entropy prior probability	*
dc.subject.other	source localization	*
dc.subject.other	UAV	*
dc.subject.other	source-filter theory	*
dc.subject.other	SPECT	*
dc.subject.other	multi fidelity	*
dc.subject.other	Artificial Intelligence (AI)	*
dc.subject.other	Monte Carlo	*
dc.subject.other	Tic-Tac	*
dc.subject.other	pragmatic hypotheses	*
dc.subject.other	cluster analysis	*
dc.subject.other	aortic dissection	*
dc.subject.other	physics-informed methods	*
dc.subject.other	UFO	*
dc.subject.other	HMC	*
dc.subject.other	steady-state	*
dc.subject.other	mean shift method	*
dc.subject.other	Bayes	*
dc.subject.other	Nimitz	*
dc.subject.other	image reconstruction	*
dc.subject.other	machine learning	*
dc.subject.other	local statistical regularization	*
dc.subject.other	marginal likelihood	*
dc.subject.other	detrending	*
dc.subject.other	Gaussian processes	*
dc.subject.other	kernel methods	*
dc.subject.other	partial differential equations	*
dc.subject.other	hypothesis tests	*
dc.subject.other	PET	*
dc.title	MaxEnt 2019—Proceedings, 2019, MaxEnt 2019The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering	*
dc.type	book
oapen.identifier.doi	10.3390/books978-3-03928-477-1	*
oapen.relation.isPublishedBy	46cabcaa-dd94-4bfe-87b4-55023c1b36d0	*
oapen.relation.isbn	9783039284771	*
oapen.relation.isbn	9783039284764	*
oapen.pages	312	*
oapen.edition	1st	*

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