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Graphical models in statistics and econometrics
Hubálek, Ondřej ; Zouhar, Jan (advisor) ; Bisová, Sára (referee)
Graphical models in statistics and econometrics provide capability to describe causal relations using causal graph in classical regression analysis and others econometric tools. Goal of this thesis is description of causal modelling of time series with help of structural models of vector autoregression. There is description of procedure of building structural VAR model, principle of graphical models and building model for causal dependence analysis. For purpose of comparison there are used data from both USA and Czech Republic and comparison of similar models for both countries is presented. Best models are then selected, to show causal relations between macroeconomic variables. For purpose of analysis, impulse-response functions are used to show impact of demand shock on GDP and other macro indicators.
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On Weakness of Evidential Networks
Vejnarová, Jiřina
In evidence theory several counterparts of Bayesian networks based on different paradigms have been proposed. We will present, through simple examples, problems appearing in two kinds of these models caused either by the conditional independence concept (or its misinterpretation) or by the use of a conditioning rule. The latter kind of problems can be avoided if undirected models are used instead.
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A Note on Factorization of Belief Functions
Jiroušek, Radim ; Shenoy, P. P.
The paper compares two main types of factorization of belief functions (one based on the Dempster´s rule of combination, the other based on the operator of composition) and shows that both the approaches are equivalent to each other in case of unconditional factorization and shows what are the differences when overlapping factorization is studied.
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Detection of independence relations from persegrams
Jiroušek, Radim
Procedures for computation with multidimensional models (multidimensional probability distributions) are efficient only for models with specific independence structure. Therefore, it is of great importance to learn the independence structure. The paper presents a solution of this task for models represented by generating sequences.
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