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PR
2011
12 years 8 months ago
Generalized darting Monte Carlo
One of the main shortcomings of Markov chain Monte Carlo samplers is their inability to mix between modes of the target distribution. In this paper we show that advance knowledge ...
Cristian Sminchisescu, Max Welling
APIN
2004
107views more  APIN 2004»
13 years 5 months ago
Designing Polymer Blends Using Neural Networks, Genetic Algorithms, and Markov Chains
In this paper we present a new technique to simulate polymer blends that overcomes the shortcomings in polymer system modeling. This method has an inherent advantage in that the v...
N. K. Roy, Walter D. Potter, D. P. Landau
GI
2009
Springer
13 years 3 months ago
An Improved Method for creating Shared Belief in Communication Constrained Sensor Networks
: Networked systems that gather sensor data in order to react to phenomena in their surroundings are faced with a growing need for adaptive behavior to operate in dynamically chang...
Eelke van Foeken, Peter Hiemstra, Leon Kester
SJ
2011
13 years 29 days ago
Analyzing the Resilience of Complex Supply Network Topologies Against Random and Targeted Disruptions
—In this paper, we study the resilience of supply networks against disruptions and provide insights to supply chain managers on how to construct a resilient supply network from t...
Kang Zhao, Akhil Kumar, Terry P. Harrison, John Ye...
DSN
2005
IEEE
13 years 11 months ago
Lumping Matrix Diagram Representations of Markov Models
Continuous-time Markov chains (CTMCs) have been used successfully to model the dependability and performability of many systems. Matrix diagrams (MDs) are known to be a space-efï¬...
Salem Derisavi, Peter Kemper, William H. Sanders