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The problem of solving general Bayesian influence diagrams is well known to be NP-complete, whence looking for efficient approximate stochastic techniques yielding suboptimal solutions in reasonable time is well justified. The purpose of this paper is to propose a new stochastic algorithm for strategy optimisation in Bayesian influence diagrams. The… (More)

The goal of the ramified optimal transport is to find an optimal transport path between two given probability measures. One measure can be identified with a source while the other one with a target. The problem is well known to be NP–hard. We develop an algorithm for solving a ramified optimal transport problem within the framework of Bayesian networks. It… (More)

The problem of learning Bayesian network structure is well known to be NP–hard. It is therefore very important to develop efficient approximation techniques. We introduce an algorithm that within the framework of influence diagrams translates the structure learning problem into the strategy optimisation problem, for which we apply the Chen's self–annealing… (More)

We propose an algorithm for determining optimal transition paths between given configurations of systems consisting of many objects. It is based on the Principle of Least Action and variational equations for Freidlin–Wentzell action functionals in Gaussian networks setup. We use our method to construct a system controlling motion and redeployment between… (More)

- T. Schorr, M. Matuszak, W. Sauer-Greff, R. Urbansky
- 2007

Iterative " turbo " decoding of parallel or serial concatenated convolutional codes (CC) allows for a performance close to Shannon's channel capacity limit. Whereas " turbo " equalization (TE) for intersymbol interference (ISI) channels corresponds to serial concatenation, we show that the turbo decoding principle for parallel concatenation can be extended… (More)

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