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CSDA
2010
118views more  CSDA 2010»
13 years 6 months ago
Grapham: Graphical models with adaptive random walk Metropolis algorithms
Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementat...
Matti Vihola
KDD
2004
ACM
170views Data Mining» more  KDD 2004»
14 years 1 days ago
Estimating the size of the telephone universe: a Bayesian Mark-recapture approach
Mark-recapture models have for many years been used to estimate the unknown sizes of animal and bird populations. In this article we adapt a finite mixture mark-recapture model i...
David Poole
IPPS
2008
IEEE
14 years 1 months ago
Reducing the run-time of MCMC programs by multithreading on SMP architectures
The increasing availability of multi-core and multiprocessor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Mont...
Jonathan M. R. Byrd, Stephen A. Jarvis, A. H. Bhal...
ICCAD
2009
IEEE
152views Hardware» more  ICCAD 2009»
13 years 4 months ago
Adaptive sampling for efficient failure probability analysis of SRAM cells
In this paper, an adaptive sampling method is proposed for the statistical SRAM cell analysis. The method is composed of two components. One part is the adaptive sampler that manip...
Javid Jaffari, Mohab Anis
CCE
2004
13 years 6 months ago
Improving convergence of the stochastic decomposition algorithm by using an efficient sampling technique
This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-st...
José María Ponce-Ortega, Vicente Ric...