Results 231 to 240 of about 471,184 (284)

Improving statistical reporting in psychology. [PDF]

Commun Psychol
Schubert AL, Steinhilber M, Kang H, Quintana DS.   +3 more
europepmc   +1 more source

Modeling approaches for estimating the effects of risk factors using longitudinal lifecourse exposure data in dementia research. [PDF]

Alzheimers Dement
Nichols E, Bindas A, Atshan S, Chang H, Chiang YY, Claus Henn B, Hayes-Larson E, Keller KP, Kezios KL, Shih RA, Szpiro AA, Weiss J, Adar SD, Knapp DM, Lee J, Weuve J.   +15 more
europepmc   +1 more source

Increased Baseline Pupil Size Linked to Uncertainty Avoidance in Decision Making. [PDF]

Eur J Neurosci
Kakaei E, Schlecht A, Hauser TU.
europepmc   +1 more source

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New anisotropic a priori error estimates

Numerische Mathematik, 2001
We prove a priori anisotropic estimates for the $L^2$ and $H^1$ interpolation error on linear finite elements. The full information about the mapping from a reference element is employed to separate the contribution to the elemental error coming from different directions. This new
FORMAGGIA, LUCA, PEROTTO, SIMONA
openaire   +2 more sources

A Priori Inverse Operator Estimation for Guaranteed Error Estimate

Proceedings of the 4th International Workshop on Reliable Engineering Computing Robust Design – Coping with Hazards, Risk and Uncertainty – REC 2010, 2010
A guaranteed error estimate procedure for linear or nonlinear two-point boundary value problems is established by authors. ‘Guaranteed’ error estimate is rigorous, i.e. it takes into account every error such as the discretization error and the rounding error when we compute an approximate solution.
Kubo T., Oishi S., Takayasu A.
openaire   +1 more source

New A Priori FEM Error Estimates for Eigenvalues

SIAM Journal on Numerical Analysis, 2006
We analyze the Ritz--Galerkin method for symmetric eigenvalue problems and prove a priori eigenvalue error estimates. For a simple eigenvalue, we prove an error estimate that depends mainly on the approximability of the corresponding eigenfunction and provide explicit values for all constants.
Andrew V. Knyazev, John E. Osborn
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A Priori Estimates of the Generalization Error for Autoencoders

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Autoencoder is a machine learning model which aims for dimensionality reduction, by reconstructing its input through a bottleneck with lower dimension than the input. It is among the most popular models used in unsupervised learning and semi-supervised learning. In this paper, we build theoretical understanding about autoencoders.
Zehao Don, Weinan E., Chao Ma
openaire   +1 more source

Unified a Priori Error Estimate and a Posteriori Error Estimate of CIP-FEM for Elliptic Equations

Advances in Applied Mathematics and Mechanics, 2016
AbstractThis paper is devoted to a unified a priori and a posteriori error analysis of CIP-FEM (continuous interior penalty finite element method) for second-order elliptic problems. Compared with the classic a priori error analysis in literature, our technique can easily apply for any type regularity assumption on the exact solution, especially for ...
Wang, Jianye, Ma, Rui
openaire   +2 more sources