MaxEnt 2019-Proceedings, 2019, MaxEnt 2019The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Von Toussaint, Udo
MaxEnt 2019-Proceedings, 2019, MaxEnt 2019The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (312 p.)
Open Access
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.
Creative Commons
English
books978-3-03928-477-1 9783039284771 9783039284764
10.3390/books978-3-03928-477-1 doi
uncertainty quantification orthodontics evidence global statistical regularization MCMC field reconstruction meshless methods annealed importance sampling cervical vertebra maturation Bayesian evidence spectral expansion non-intrusive model comparison plasma-wall interactions nested sampling Deep Learning (DL) classification stochastic gradients Bayesian Maximum a Posteriori approach Convolutional Neural Network (CNN) impedance cardiography vowel SGHMC Gaussian process regression precise hypotheses formant Bayesian analysis thermodynamic Integration model averaging probability theory acoustic phonetics UAP entropy prior probability source localization UAV source-filter theory SPECT multi fidelity Artificial Intelligence (AI) Monte Carlo Tic-Tac pragmatic hypotheses cluster analysis aortic dissection physics-informed methods UFO HMC steady-state mean shift method Bayes Nimitz image reconstruction machine learning local statistical regularization marginal likelihood detrending Gaussian processes kernel methods partial differential equations hypothesis tests PET
MaxEnt 2019-Proceedings, 2019, MaxEnt 2019The 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - MDPI - Multidisciplinary Digital Publishing Institute 2020 - 1 electronic resource (312 p.)
Open Access
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.
Creative Commons
English
books978-3-03928-477-1 9783039284771 9783039284764
10.3390/books978-3-03928-477-1 doi
uncertainty quantification orthodontics evidence global statistical regularization MCMC field reconstruction meshless methods annealed importance sampling cervical vertebra maturation Bayesian evidence spectral expansion non-intrusive model comparison plasma-wall interactions nested sampling Deep Learning (DL) classification stochastic gradients Bayesian Maximum a Posteriori approach Convolutional Neural Network (CNN) impedance cardiography vowel SGHMC Gaussian process regression precise hypotheses formant Bayesian analysis thermodynamic Integration model averaging probability theory acoustic phonetics UAP entropy prior probability source localization UAV source-filter theory SPECT multi fidelity Artificial Intelligence (AI) Monte Carlo Tic-Tac pragmatic hypotheses cluster analysis aortic dissection physics-informed methods UFO HMC steady-state mean shift method Bayes Nimitz image reconstruction machine learning local statistical regularization marginal likelihood detrending Gaussian processes kernel methods partial differential equations hypothesis tests PET