This paper reports simulation experiments, applying the cross entropy method suchas the importance sampling algorithm for efficient estimation of rare event probabilities in Markovian reliability systems. The method is compared to various failurebiasing schemes that have been proved to give estimators with bounded relativeerrors. The results from the experiments indicate a considerable improvement ofthe performance of the importance sampling estimators, where performance is mea-sured by the relative error of the estimate, by the relative error of the estimator,and by the gain of the importance sampling simulation to the normal simulation
Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to ...
While search is normally modelled by economists purely in terms of decisions over making observation...
A recent kinetic mechanism (Sarathy et al., 2012) describing the low temperature oxidation of n-buta...
AbstractIn this paper a Markov model for Evolutionary Multi-Agent System is recalled. The model allo...
Micro-task Crowdsourcing has been used for different purposes: creating training data for machine le...
Collecting very large amount of data from experimental multiphase measurement is a common practice i...
Computational inverse problems related to partial differential equations (PDEs) often contain nuisan...
Boolean random sets are versatile tools to match morphological and topological properties of real st...
Many of the major advances in our understanding of how functional brain imaging signals relate to ne...
In this paper, we study the model selection and structure specification for the generalised semi-var...
Machine learning is a hot topic in today's society. Data sets of varying sizes show up in a number o...
The design of human–robot interactions is a key challenge to optimize operational performance. A pro...
Stochastic simulation of large-scale biochemical reaction networks is becoming essential for Systems...
A computational simplification of the Kalman filter (KF) is introduced – the parametric Kalman filte...
The aim of my study was to investigate the maintenance of variability for sperm production in the gu...
Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to ...
While search is normally modelled by economists purely in terms of decisions over making observation...
A recent kinetic mechanism (Sarathy et al., 2012) describing the low temperature oxidation of n-buta...
AbstractIn this paper a Markov model for Evolutionary Multi-Agent System is recalled. The model allo...
Micro-task Crowdsourcing has been used for different purposes: creating training data for machine le...
Collecting very large amount of data from experimental multiphase measurement is a common practice i...
Computational inverse problems related to partial differential equations (PDEs) often contain nuisan...
Boolean random sets are versatile tools to match morphological and topological properties of real st...
Many of the major advances in our understanding of how functional brain imaging signals relate to ne...
In this paper, we study the model selection and structure specification for the generalised semi-var...
Machine learning is a hot topic in today's society. Data sets of varying sizes show up in a number o...
The design of human–robot interactions is a key challenge to optimize operational performance. A pro...
Stochastic simulation of large-scale biochemical reaction networks is becoming essential for Systems...
A computational simplification of the Kalman filter (KF) is introduced – the parametric Kalman filte...
The aim of my study was to investigate the maintenance of variability for sperm production in the gu...
Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to ...
While search is normally modelled by economists purely in terms of decisions over making observation...
A recent kinetic mechanism (Sarathy et al., 2012) describing the low temperature oxidation of n-buta...