Importance sampling for stochastic simulations
Management Science
IEEE/ACM Transactions on Networking (TON)
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Ant-based load balancing in telecommunications networks
Adaptive Behavior
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Analysis of state-independent importance-sampling measures for the two-node tandem queue
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Importance sampling in Markovian settings
WSC '05 Proceedings of the 37th conference on Winter simulation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Markovian ants in a queuing system
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Coupling and importance sampling for statistical model checking
TACAS'12 Proceedings of the 18th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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Importance Sampling is a potentially powerful variance reduction technique to speed up simulations where the objective depends on the occurrence of rare events. However, it is crucial to find a change of the underlying probability measure yielding estimators with significantly reduced variance compared to direct estimators. In this paper, we present a new dynamic and adaptive method for this purpose. The method is inspired by ant-based systems that are in widespread use for solving optimization problems. No intimate knowledge of the model under consideration is necessary. Instead, the method adapts to it. Different commonly used modeling paradigms such as queueing and reliability models, amongst many others, are supported by describing the new method in terms of a transition class formalism. Simulation results demonstrate the accuracy of the obtained estimates, and details of the adapted change of measure are investigated to gain insights into the inner workings of the method.