Results 71 to 80 of about 70,196 (284)
Quantifying parameter uncertainties in optical scatterometry using Bayesian inversion
, 2017 We present a Newton-like method to solve inverse problems and to quantify parameter uncertainties. We apply the method to parameter reconstruction in optical scatterometry, where we take into account a priori information and measurement uncertainties ...
Bodermann, B. +5 more
core +1 more source
Bayesian Inversion of Geoelectrical Resistivity Data [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2003 SummaryEnormous quantities of geoelectrical data are produced daily and often used for large scale reservoir modelling. To interpret these data requires reliable and efficient inversion methods which adequately incorporate prior information and use realistically complex modelling structures.
Andersen, Kim Emil +2 more
openaire +2 more sources
Hierarchical Summary Statistics Encoding Across Primary Visual and Posterior Parietal Cortices
Advanced Science, EarlyView.This study shows that mouse V1 simultaneously encodes the ensemble mean and variance of motion, providing a robust summary‐statistic representation that persists despite single‐neuron variability. These signals propagate to PPC, where they are transformed into abstract category representations during decision making.
Young‐Beom Lee +4 more
wiley +1 more source
Bayesian inverse problems with heterogeneous variance
Scandinavian Journal of Statistics, 2023 AbstractWe consider inverse problems in Hilbert spaces under correlated Gaussian noise, and use a Bayesian approach to find their regularized solution. We focus on mildly ill‐posed inverse problems with fractional noise, using a novel wavelet‐based vaguelette–vaguelette approach.
Natalia Bochkina, Jenovah Rodrigues
openaire +3 more sources
Advanced Science, EarlyView.Mid‐infrared optoacoustic microscopy (MiROM) acquires lipid‐ and protein‐ associated vibrational contrast in intact fat tissue without dyes, preserving native tissue architecture. Through lateral and axial segmentation, MiROM tracks intrinsic intracellular changes during postnatal remodeling. A quantitative spatial analysis tool (Q‐SAT) maps white‐ and
Myeongseop Kim +7 more
wiley +1 more source
Variational Bayesian inversion for microwave breast imaging
Computer Assisted Methods in Engineering and Science, 2017 Microwave imaging is considered as a nonlinear inverse scattering problem and tackled in a Bayesian estimation framework. The object under test (a breast affected by a tumor) is assumed to be composed of compact regions made of a restricted number of ...
Leila Gharsalli +3 more
doaj +1 more source
Finding patterns in subsurface using Bayesian machine learning approach
Underground Space, 2020 Stochastic simulation approaches and uncertainty quantification are usually adopted for gaining insight into variability in soil stratigraphy configurations.
Hui Wang
doaj +1 more source
Generalized Modes in Bayesian Inverse Problems [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2019 Uncertainty quantification requires efficient summarization of high- or even infinite-dimensional (i.e., non-parametric) distributions based on, e.g., suitable point estimates (modes) for posterior distributions arising from model-specific prior distributions. In this work, we consider non-parametric modes and MAP estimates for priors that do not admit
Christian Clason +3 more
openaire +3 more sources
Advanced Science, EarlyView.Skeleton‐oriented object segmentation (SKOOTS) introduces a new strategy for 3D mitochondrial instance segmentation by predicting explicit skeletons rather than relying on boundary cues. This approach enables robust analysis of densely packed organelles in large FIB‐SEM datasets.
Christopher J. Buswinka +3 more
wiley +1 more source
Bayesian spatiotemporal modeling for inverse problems
Statistics and Computing, 2023 38 pages, 23 ...
Shiwei Lan, Shuyi Li, Mirjeta Pasha
openaire +2 more sources