Results 31 to 40 of about 70,196 (284)
Inference of internal stress in a cell monolayer [PDF]
, 2016 We combine traction force data with Bayesian inversion to obtain an absolute estimate of the internal stress field of a cell monolayer. The method, Bayesian inversion stress microscopy (BISM), is validated using numerical simulations performed in a wide ...
Ishihara, S. +5 more
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, 2020 Bayesian inverse modeling is important for a better understanding of hydrological processes. However, this approach can be computationally demanding, as it usually requires a large number of model evaluations.
Chen, Dingjiang +4 more
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Fast Gibbs sampling for high-dimensional Bayesian inversion [PDF]
, 2016 Solving ill-posed inverse problems by Bayesian inference has recently attracted considerable attention. Compared to deterministic approaches, the probabilistic representation of the solution by the posterior distribution can be exploited to explore and ...
Burger M +15 more
core +2 more sources
Stein Variational Reduced Basis Bayesian Inversion [PDF]
SIAM Journal on Scientific Computing, 2021 We propose and analyze a Stein variational reduced basis method (SVRB) to solve large-scale PDE-constrained Bayesian inverse problems. To address the computational challenge of drawing numerous samples requiring expensive PDE solves from the posterior distribution, we integrate an adaptive and goal-oriented model reduction technique with an ...
Peng Chen, Omar Ghattas
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A Novel Approach for Bathymetry Estimation through Bayesian Gravity Inversion
Geosciences, 2023 The bathymetry is the most superficial layer of the Earth’s crust on which it is possible to perform direct measurements. However, it is also well known that water covers more than 70% of the Earth’s surface, so an enormous expenditure of acquisition ...
Daniele Sampietro, Martina Capponi
doaj +1 more source
The Einstein Ring 0047-2808 Revisited: A Bayesian Inversion [PDF]
, 2006 In a previous paper, we outlined a new Bayesian method for inferring the properties of extended gravitational lenses, given data in the form of resolved images.
B. J. Brewer +5 more
core +2 more sources
Parallelized Adaptive Importance Sampling for Solving Inverse Problems
Frontiers in Earth Science, 2018 In the field of groundwater hydrology and more generally geophysics, solving inverse problems in a complex, geologically realistic, and discrete model space often requires the usage of Monte Carlo methods.
Christoph Jäggli +2 more
doaj +1 more source
Bayesian Posterior Contraction Rates for Linear Severely Ill-posed Inverse Problems [PDF]
, 2013 We consider a class of linear ill-posed inverse problems arising from inversion of a compact operator with singular values which decay exponentially to zero. We adopt a Bayesian approach, assuming a Gaussian prior on the unknown function.
Agapiou, Sergios +2 more
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Bayesian Multitask Inverse Reinforcement Learning [PDF]
, 2012 We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is to formalise the problem as statistical preference elicitation, via a number of structured priors,
Dimitrakakis C., Rothkopf C.A.
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Bayesian Approach to Inverse Quantum Statistics [PDF]
Physical Review Letters, 2000 A nonparametric Bayesian approach is developed to determine quantum potentials from empirical data for quantum systems at finite temperature. The approach combines the likelihood model of quantum mechanics with a priori information over potentials implemented in form of stochastic processes.
Lemm, J. C., Uhlig, J., Weiguny, A.
openaire +3 more sources