Results 81 to 90 of about 800 (115)
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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 +7 more
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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 +7 more
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Bayesian Estimation of Inverse Dose Response
Biometrics, 2008Summary Inverse dose–response estimation refers to the inference of an effective dose of some agent that gives a desired probability of response, say 0.5. We consider inverse dose response for two agents, an application that has not received much attention in the literature.
Hu, Bo, Ji, Yuan, Tsui, Kam-Wah
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On Bayesian inference for the Inverse Gaussian distribution
Statistics & Probability Letters, 1991zbMATH Open Web Interface contents unavailable due to conflicting licenses.
BETRO B, ROTONDI R
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Bayesian inference for inverse problems – statistical inversion
e & i Elektrotechnik und Informationstechnik, 2007Unlike deterministic inversion methods, statistical approaches are capable of taking into account inherent measurement and model uncertainties into the inverse problem solution in a very simple and natural way. Statistical inversion theory reformulates inverse problems as problems of Bayesian statistical inference. In this framework, the solution to an
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Bayesian geoacoustic inversion.
The Journal of the Acoustical Society of America, 2010This 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
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Geophysics, 2011
Abstract 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.
Arild Buland +2 more
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Abstract 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.
Arild Buland +2 more
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Bayesian Networks for Inverse Inference in Manufacturing Bayesian Networks
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017Physics based simulations of manufacturing processes are used for prediction of material properties and defects in a number of industrial applications. However, a practising engineer often requires the solution to an "inverse problem" - prediction of inputs for the desired outcome.
Avadhut Sardeshmukh +3 more
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Bayesian Gaussian Mixture Linear Inversion in Geophysical Inverse Problems
Proceedings, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dario Grana +2 more
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Full‐Bayesian Inversion of the Edwards Aquifer
Groundwater, 2004Abstract The Bayesian inverse approach proposed by is extended to estimate the transmissivity fields of highly heterogeneous aquifers for steady state ground water flow. Boundary conditions are Dirichlet and Neumann type, and sink and source terms are included.
Yefang, Jiang +2 more
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2022
We consider linear, mildly ill-posed inverse problems in separable Hilbert spaces under Gaussian noise, whose covariance operator is not identity (i.e. it is not a white noise problem), and use the Bayesian approach to nd their regularised solution. Speci cally, our goal is to regularise the prior in such a way that the posterior distribution achieves ...
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We consider linear, mildly ill-posed inverse problems in separable Hilbert spaces under Gaussian noise, whose covariance operator is not identity (i.e. it is not a white noise problem), and use the Bayesian approach to nd their regularised solution. Speci cally, our goal is to regularise the prior in such a way that the posterior distribution achieves ...
openaire +2 more sources