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Několik poznámek a otázek k Josangově uhlazenému zhrubění.
Daniel, Milan
Definitions of two different ways of coarsening of basic belief assignments to opinions - simple coarsening and smooth coarsening – are recalled in this contribution. A relation of results of combination of smooth opinions using the consensus operator to belief functions on an original $n$-element frame of discernment is examined. A question of an interpretation of smoothly coarsened opinions is discussed.
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Možnosti Iterativní proporcionální fitující procedůry
Vejnarová, Jiřina
Iterative Proportional Fitting Procedure (parameterized by a continuous t-norm) for computation of multidimensional possibility distributions from its marginals designed in 2003, is discussed in more details. Special attention is paid to its application to possibilistic marginal problem.
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Podmíněná nezávislost ve věrohodnostních funkcích: Příklady
Jiroušek, Radim
The paper presents an additional possibility how to define conditional independence relation for belief functions. The approach is based on the operator of composition originally designed for multidimensional model processing. Not to make confusion with the preceding definitions we call this relation conditional irrelevance. In the paper examples illustrating properties of this relation are presented.
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Efektivní algoritmus na hledání redukcí v kompozicionálních modelech
Kratochvíl, Václav
This paper deals with the problem of marginalization of multidimensional probability distributions represented by a compositional model. By the perfect one in this case. From the computational point of view this solution is more efficient than any known marginalization process for Bayesian models. This is because the process mentioned in the paper in a form of an algorithm and takes an advantage of the fact that the perfect sequence models have some information encoded; if can be obtained from the Bayesian networks by an application of rather computationally expensive procedures. One part of that algorithm is marginalization by means of reduction. This paper describe a new faster algorithm to find a reduction in a compositional model.
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Využití imsetů při učení bayesovských sítí
Vomlel, Jiří ; Studený, Milan
This paper describes a modification of the greedy equivalence search (GES) algorithm. The presented modification is based on the algebraic approach to learning. The states of the search space are standard imsets. Each standard imset represents an equivalence class of Bayesian networks. For a given quality criterion the database is represented by the respective data imset. This allows a very simple update of a given quality criterion since the moves between states are represented by differential imsets. We exploit a direct characterization of lower and upper inclusion neighborhood, which allows an efficient search for the best structure in the inclusion neighborhood. The algorithm was implemented in R and is freely available.
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