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

• Main Author: Von Toussaint, Udo (auth)
• Other Authors: Preuss, Roland (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Subjects: 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

Summary

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.

Physical Description

1 electronic resource (312 p.)

ISBN

books978-3-03928-477-1
9783039284771
9783039284764

Access

Open Access