Results 271 to 280 of about 846,274 (324)

Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection. [PDF]

Front Netw Physiol
Baratpur AZ, Vahdat-Nejad H, Arslan E, Hassannataj Joloudari J, Gaftandzhieva S.   +4 more
europepmc   +1 more source

Learning in higher dimensions: a strategy for alloy electrocatalyst discovery.

EES Catal
Mints VA, Pedersen JK, Wiberg GKH, Edelvang-Pejrup J, Gautam D, Jensen KMØ, Rossmeisl J, Arenz M.   +7 more
europepmc   +1 more source

Deep Bayesian Inversion

arXiv.org, 2018
Characterizing statistical properties of solutions of inverse problems is essential for decision making.
J. Adler, O. Öktem
semanticscholar   +3 more sources

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Fast Bayesian Inversion of Airborne Electromagnetic Data Based on the Invertible Neural Network

IEEE Transactions on Geoscience and Remote Sensing, 2023
The inversion of airborne electromagnetic (AEM) data suffers from severe nonuniqueness in the solution. Bayesian inference provides the means to estimate structural uncertainty with a rich suite of statistical information.
Sihong Wu, Qinghua Huang, Li Zhao
semanticscholar   +1 more source

Accelerated Bayesian Inversion of Transient Electromagnetic Data Using MCMC Subposteriors

IEEE Transactions on Geoscience and Remote Sensing, 2021
Transient electromagnetic method (TEM) is one of the major tools to image the subsurface resistivity. The gradient-based inversion of TEM data only provides a unique solution using a subjectively defined regularization penalty, leaving the uncertainty of
Hai Li, G. Xue, Linbo Zhang
semanticscholar   +1 more source

A Julia software package for transdimensional Bayesian inversion of electromagnetic data over horizontally stratified media

Geophysics, 2022
A quantitative assessment of model parameter uncertainty is vital for a reliable interpretation of electromagnetic (EM) data due to the nonuniqueness inherent to EM inverse problems.
Ronghua Peng, B. Han, Yajun Liu, Xiangyun Hu   +3 more
semanticscholar   +1 more source

Bayesian inversion whispers

The Leading Edge, 2008
Many times we are faced with the business decision of whether or not to develop a sand that is at the limit of seismic resolution and near the noise level of the data. The critical issue is developing a reasonable certainty that there is enough volume of hydrocarbons to develop. A popular approach is to use Bayesian methods to determine the probability
Michael E. Glinsky, Mark C. Haase, Valerie Charoing, Guy Duncan, Robin Hill, Gerry O'Halloran, Long Dang, James Gunning   +7 more
openaire   +2 more sources

Bayesian geoacoustic inversion.

The Journal of the Acoustical Society of America, 2010
This paper describes a general Bayesian approach to estimating seabed geoacoustic parameters from ocean acoustic data, which is also applicable to other inverse problems. Within a Bayesian formulation, the complete solution is given by the posterior probability density (PPD), which includes both data and prior information.
Stan E. Dosso, Jan Dettmer
openaire   +1 more source

Cauchy Markov random field priors for Bayesian inversion

Statistics and computing, 2021
The use of Cauchy Markov random field priors in statistical inverse problems can potentially lead to posterior distributions which are non-Gaussian, high-dimensional, multimodal and heavy-tailed.
Jarkko Suuronen, Neil K. Chada, L. Roininen   +2 more
semanticscholar   +1 more source

Bayesian Dix inversion

GEOPHYSICS, 2011
We have developed a Bayesian method for Dix inversion and illustrated it with examples from the North Sea. The method is a constrained Dix inversion in which the uncertainty of the estimated interval velocities is an integral part of the solution. The method combines available geologic prior knowledge with the information in the picked rms velocities ...
Arild Buland, Odd Kolbjørnsen, Andrew J. Carter   +2 more
openaire   +1 more source