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- Ilya B. Gertsbakh, Reuven Rubinsteinb, Yoseph Shpungin, Radislav Vaisman
- 2013

In this paper we show how the permutation Monte Carlo method, originally developed for reliability networks, can be successfully adapted for stochastic flow networks, and in particular for estimation… (More)

- Zdravko I. Botev, Radislav Vaisman, Reuven Y. Rubinstein, Pierre L'Ecuyer
- Proceedings of the Winter Simulation Conference…
- 2014

We consider the problem of estimating the unreliability of a stochastic flow network, defined as the probability that the maximum flow value from a source node to a terminal node in a directed… (More)

- Reuven Y. Rubinstein, Andrey Dolgin, Radislav Vaisman
- Communications in Statistics - Simulation and…
- 2012

We show how a simple modification of the splitting method based on Gibbs sampler can be efficiently used for decision making in the sense that one can efficiently decide whether or not a given set of… (More)

The problem of estimating the size of a backtrack tree is an important but hard problem in computational sciences. An efficient solution of this problem can have a major impact on the hierarchy of… (More)

On the Use of Smoothing to Improve the Performance of the Splitting Method Frédéric Cérou a , Arnaud Guyader b c , Reuven Rubinstein a & Radislav Vaisman a a Faculty of Industrial Engineering and… (More)

- Ilya B. Gertsbakh, Yoseph Shpungin, Radislav Vaisman
- Springer Briefs in Electrical and Computer…
- 2014

We extend the network reliability estimation methodology based on evolution (creation) Monte Carlo into four directions: (i) introducing unreliable nodes; (ii) adjusting the evolution process with… (More)

- Radislav Vaisman, Dirk P. Kroese, Ilya B. Gertsbakh
- IEEE Transactions on Reliability
- 2016

Terminal network reliability problems appear in many real-life applications, such as transportation grids, social and computer networks, communication systems, etc. In this paper, we focus on… (More)

We consider a monotone binary system with ternary components. ”Ternary” means that each component can be in one of three states: up, middle (mid) and down. Handling such systems is a hard task, even… (More)

1. In spite of the common consensus on the classic Markov chain Monte Carlo (MCMC) as a universal tool for generating samples on complex sets, it fails to generate points uniformly distributed on… (More)