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- Michal Matuszak, Tomasz Schreiber
- ICAISC
- 2010

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)

- Michal Matuszak, Jacek Miekisz, Tomasz Schreiber
- ICAISC
- 2012

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)

- T Long, M Matuszak, M Feng, B A Fraass, R K Ten Haken, H E Romeijn
- Medical physics
- 2012

PURPOSE
To introduce a method to efficiently identify and calculate meaningful tradeoffs between criteria in an interactive IMRT treatment planning procedure. The method provides a systematic approach to developing high-quality radiation therapy treatment plans.
METHODS
Treatment planners consider numerous dosimetric criteria of varying importance that,… (More)

- Michal Matuszak, Jacek Miekisz
- ICANN
- 2012

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)

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