Results 161 to 170 of about 531,665 (271)
LARGE COVARIANCE ESTIMATION THROUGH ELLIPTICAL FACTOR MODELS. [PDF]
Fan J, Liu H, Wang W.
europepmc +1 more source
Abstract Despite extensive modeling efforts in extraction research, transient column models are rarely applied in industry due to concerns regarding parameter identifiability and model reliability. To address this, we analyzed uncertainty propagation from estimated parameters in a previously introduced column model and assessed identifiability via ill ...
Andreas Palmtag +2 more
wiley +1 more source
Sparse Multi-task Inverse Covariance Estimation for Connectivity Analysis in EEG Source Space. [PDF]
Liu F, Stephen EP, Prerau MJ, Purdon PL.
europepmc +1 more source
A multiscale Bayesian optimization framework for process and material codesign
Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We
Michael Baldea
wiley +1 more source
Fast covariance estimation for sparse functional data. [PDF]
Xiao L, Li C, Checkley W, Crainiceanu C.
europepmc +1 more source
Abstract Three instruments–Raman spectroscopy, attenuated total reflectance–Fourier transform infrared spectroscopy, and focused beam reflectance measurement–were used to detect sensor faults, mixing faults, and unanticipated chemistry in a system of multicomponent slurries.
Steven H. Crouse +2 more
wiley +1 more source
Information Geometry for Covariance Estimation in Heterogeneous Clutter with Total Bregman Divergence. [PDF]
Hua X, Cheng Y, Wang H, Qin Y.
europepmc +1 more source
Trust‐region filter algorithms utilizing Hessian information for gray‐box optimization
Abstract Optimizing industrial processes often involves gray‐box models that couple algebraic glass‐box equations with black‐box components lacking analytic derivatives. Such systems challenge derivative‐based solvers. The classical trust‐region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous ...
Gul Hameed +4 more
wiley +1 more source
Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression. [PDF]
Gilles MA, Singer A.
europepmc +1 more source
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi +2 more
wiley +1 more source

