Results 261 to 270 of about 3,258,064 (367)

A Divide and Conquer Algorithm of Bayesian Density Estimation

open access: yesAustralian &New Zealand Journal of Statistics, EarlyView.
ABSTRACT Datasets for statistical analysis become extremely large even when stored on one single machine with some difficulty. Even when the data can be stored in one machine, the computational cost would still be intimidating. We propose a divide and conquer solution to density estimation using Bayesian mixture modelling, including the infinite ...
Ya Su
wiley   +1 more source

Learning Metric Fields for Fast Low‐Distortion Mesh Parameterizations

open access: yesComputer Graphics Forum, EarlyView.
Abstract We present a fast and robust method for computing an injective parameterization with low isometric distortion for disk‐like triangular meshes. Harmonic function‐based methods, with their rich mathematical foundation, are widely used. Harmonic maps are particularly valuable for ensuring injectivity under certain boundary conditions. In addition,
G. Fargion, O. Weber
wiley   +1 more source

Multiphysics Simulation Methods in Computer Graphics

open access: yesComputer Graphics Forum, EarlyView.
Abstract Physics simulation is a cornerstone of many computer graphics applications, ranging from video games and virtual reality to visual effects and computational design. The number of techniques for physically‐based modeling and animation has thus skyrocketed over the past few decades, facilitating the simulation of a wide variety of materials and ...
Daniel Holz   +5 more
wiley   +1 more source

PrismBreak: Exploration of Multi‐Dimensional Mixture Models

open access: yesComputer Graphics Forum, EarlyView.
Abstract In data science, visual data exploration becomes increasingly more challenging due to the continued rapid increase of data dimensionality and data sizes. To manage complexity, two orthogonal approaches are commonly used in practice: First, data is frequently clustered in high‐dimensional space by fitting mixture models composed of normal ...
B. Zahoransky, T. Günther, K. Lawonn
wiley   +1 more source

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