Theory on semi-parametric efficient estimation in missing data problems has been systematically developed by Robins and his coauthors. Except in relatively simple problems, semi-parametric efficient ...
ABSTRACT: Parameter estimation by maximizing the marginal likelihood function in generalized linear mixed models (GLMMs) is highly challenging because it may involve analytically intractable ...
Abstract: Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface. Recently, learning-based methods have ...
An approximation is proposed for the posterior mean and standard deviation of the ability parameter in an item response model. The procedure assumes that approximations to the posterior mean and ...
Abstract: We develop a unified Bayesian framework for optical flow (OF) estimation that uses a variational lower bound to obtain a variational approximation of the posterior probability distribution.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback