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A comparison of evidential networks and compositional models

Jiřina Vejnarová (2014)

Kybernetika

Several counterparts of Bayesian networks based on different paradigms have been proposed in evidence theory. Nevertheless, none of them is completely satisfactory. In this paper we will present a new one, based on a recently introduced concept of conditional independence. We define a conditioning rule for variables, and the relationship between conditional independence and irrelevance is studied with the aim of constructing a Bayesian-network-like model. Then, through a simple example, we will...

A method for knowledge integration

Martin Janžura, Pavel Boček (1998)

Kybernetika

With the aid of Markov Chain Monte Carlo methods we can sample even from complex multi-dimensional distributions which cannot be exactly calculated. Thus, an application to the problem of knowledge integration (e. g. in expert systems) is straightforward.

A scratch removal method

Michal Haindl, Stanislava Šimberová (1998)

Kybernetika

We present a new type of scratch removal algorithm based on a causal adaptive multidimensional prediction. The predictor use available information from the failed pixel surrounding due to spectral and spatial correlation of multispectral data but not any information from failed pixel itself. Predictor parameters cannot be directly identified so a special approximation is introduced.

Analytical representation of ellipses in the Aitchison geometry and its application

Karel Hron (2009)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

Compositional data, multivariate observations that hold only relative information, need a special treatment while performing statistical analysis, with respect to the simplex as their sample space ([Aitchison, J.: The Statistical Analysis of Compositional Data. Chapman and Hall, London, 1986.], [Aitchison, J., Greenacre, M.: Biplots of compositional data. Applied Statistics 51 (2002), 375–392.], [Buccianti, A., Mateu-Figueras, G., Pawlowsky-Glahn, V. (eds): Compositional data analysis in the geosciences:...

Bounds on Capital Requirements For Bivariate Risk with Given Marginals and Partial Information on the Dependence

Carole Bernard, Yuntao Liu, Niall MacGillivray, Jinyuan Zhang (2013)

Dependence Modeling

Nelsen et al. [20] find bounds for bivariate distribution functions when there are constraints on the values of its quartiles. Tankov [25] generalizes this work by giving explicit expressions for the best upper and lower bounds for a bivariate copula when its values on a compact subset of [0; 1]2 are known. He shows that they are quasi-copulas and not necessarily copulas. Tankov [25] and Bernard et al. [3] both give sufficient conditions for these bounds to be copulas. In this note we give weaker...

Covariance Structure of Principal Components for Three-Part Compositional Data

Klára Hrůzová, Karel Hron, Miroslav Rypka, Eva Fišerová (2013)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

Statistical analysis of compositional data, multivariate observations carrying only relative information (proportions, percentages), should be performed only in orthonormal coordinates with respect to the Aitchison geometry on the simplex. In case of three-part compositions it is possible to decompose the covariance structure of the well-known principal components using variances of log-ratios of the original parts. They seem to be helpful for the interpretation of these special orthonormal coordinates....

Criterio para detectar outliers en poblaciones normales bivariantes.

Joaquón Muñoz García (1984)

Trabajos de Estadística e Investigación Operativa

Damos un procedimiento de detección de outliers para muestras procedentes de poblaciones normales bivariantes, que viene dado por el cuadrado de la distancia entre matrices de sumas de cuadrados y sumas de productos de observaciones muestrales, la cual se ha obtenido a partir de la forma métrica diferencial de Maas.

Discriminating between causal structures in Bayesian Networks given partial observations

Philipp Moritz, Jörg Reichardt, Nihat Ay (2014)

Kybernetika

Given a fixed dependency graph G that describes a Bayesian network of binary variables X 1 , ⋯ , X n , our main result is a tight bound on the mutual information I c ( Y 1 , ⋯ , Y k ) = ∑ j = 1 k H ( Y j ) / c - H ( Y 1 , ⋯ , Y k ) of an observed subset Y 1 , ⋯ , Y k of the variables X 1 , ⋯ , X n . Our bound depends on certain quantities that can be computed from the connective structure of the nodes in G . Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.

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