26,648 research outputs found
Monte Carlo likelihood inference for missing data models
We describe a Monte Carlo method to approximate the maximum likelihood
estimate (MLE), when there are missing data and the observed data likelihood is
not available in closed form. This method uses simulated missing data that are
independent and identically distributed and independent of the observed data.
Our Monte Carlo approximation to the MLE is a consistent and asymptotically
normal estimate of the minimizer of the Kullback--Leibler
information, as both Monte Carlo and observed data sample sizes go to infinity
simultaneously. Plug-in estimates of the asymptotic variance are provided for
constructing confidence regions for . We give Logit--Normal
generalized linear mixed model examples, calculated using an R package.Comment: Published at http://dx.doi.org/10.1214/009053606000001389 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A new automated workflow for 3D character creation based on 3D scanned data
In this paper we present a new workflow allowing the creation of 3D characters in an automated way that does not require the expertise of an animator. This workflow is based of the acquisition of real human data captured by 3D body scanners, which is them processed to generate firstly animatable body meshes, secondly skinned body meshes and finally textured 3D garments
- âŚ