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IOR
2008
91views more  IOR 2008»
13 years 7 months ago
A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains
We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in the chain can be random an...
Pierre L'Ecuyer, Christian Lécot, Bruno Tuf...
CVPR
2009
IEEE
14 years 6 months ago
Markov Chain Monte Carlo Combined with Deterministic Methods for Markov Random Field Optimization
Many vision problems have been formulated as en- ergy minimization problems and there have been signif- icant advances in energy minimization algorithms. The most widely-used energ...
Wonsik Kim (Seoul National University), Kyoung Mu ...
ICCV
2001
IEEE
14 years 9 months ago
Image Segmentation by Data Driven Markov Chain Monte Carlo
?This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contribut...
Zhuowen Tu, Song Chun Zhu, Heung-Yeung Shum
SIGSOFT
2007
ACM
14 years 8 months ago
State space exploration using feedback constraint generation and Monte-Carlo sampling
The systematic exploration of the space of all the behaviours of a software system forms the basis of numerous approaches to verification. However, existing approaches face many c...
Sriram Sankaranarayanan, Richard M. Chang, Guofei ...
SIGMOD
2008
ACM
169views Database» more  SIGMOD 2008»
14 years 7 months ago
MCDB: a monte carlo approach to managing uncertain data
To deal with data uncertainty, existing probabilistic database systems augment tuples with attribute-level or tuple-level probability values, which are loaded into the database al...
Ravi Jampani, Fei Xu, Mingxi Wu, Luis Leopoldo Per...