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» Introduction to Monte Carlo simulation
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CVPR
2010
IEEE
13 years 6 months ago
Abrupt motion tracking via adaptive stochastic approximation Monte Carlo sampling
Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (...
Xiuzhuang Zhou and Yao Lu
77
Voted
WSC
1997
14 years 11 months ago
Descriptive Sampling: An Improvement over Latin Hypercube Sampling
Descriptive Sampling (DS), a Monte Carlo sampling technique based on a deterministic selection of the input values and their random permutation, represents a deep conceptual chang...
Eduardo Saliby
70
Voted
ASPDAC
2007
ACM
133views Hardware» more  ASPDAC 2007»
15 years 1 months ago
Modeling Sub-90nm On-Chip Variation Using Monte Carlo Method for DFM
- For sub-90nm technology nodes and below, random fluctuations of within-die physical process properties are also known as random on-chip variation (OCV). It impacts on the VLSI/So...
Jun-Fu Huang, Victor C. Y. Chang, Sally Liu, Kelvi...
92
Voted
CISS
2008
IEEE
15 years 4 months ago
Near optimal lossy source coding and compression-based denoising via Markov chain Monte Carlo
— We propose an implementable new universal lossy source coding algorithm. The new algorithm utilizes two wellknown tools from statistical physics and computer science: Gibbs sam...
Shirin Jalali, Tsachy Weissman
ICRA
2005
IEEE
150views Robotics» more  ICRA 2005»
15 years 3 months ago
Learning Sensor Network Topology through Monte Carlo Expectation Maximization
— We consider the problem of inferring sensor positions and a topological (i.e. qualitative) map of an environment given a set of cameras with non-overlapping fields of view. In...
Dimitri Marinakis, Gregory Dudek, David J. Fleet