Titlebook: Symbolic and Quantitative Approaches to Reasoning with Uncertainty; 7th European Confere Thomas Dyhre Nielsen,Nevin Lianwen Zhang Conferenc

Osteoarthritis

Introducing Situational Influences in QPNsled by the ambiguous sign ‘?’, which indicates that the actual sign of the influence depends on the current state of the network. The presence of influences with such ambiguous signs tends to lead to ambiguous results upon inference. In this paper we introduce the concept of situational influence in

百灵鸟

FLACK

ARIA

Morphing the Hugin and Shenoy–Shafer Architecturesnd query answering power. The Hugin architecture is more time–efficient on arbitrary jointrees, avoiding some redundant computations performed by the Shenoy–Shafer architecture. This efficiency, however, comes at the price of limiting the number of queries the Hugin architecture is capable of answer

LAIR

Characterization of Inclusion Neighbourhood in Terms of the Essential Graph: Upper Neighbourss. In this paper, neighbouring equivalence classes of a given equivalence class of Bayesian networks are characterized efficiently by means of the respective essential graph. The characterization reveals hidded internal structure of the inclusion neighbourhood. More exactly, upper neighbours, that i

endure

Approximating Conditional MTE Distributions by Means of Mixed Treesnetworks. One of the features of the MTE model is that standard propagation algorithms as Shenoy-Shafer and Lazy propagation can be used. Estimating conditional MTE densities from data is a rather difficult problem since, as far as we know, such densities cannot be expressed in parametric form in th

Nebulizer

Effective Dimensions of Partially Observed Polytreescan be measured by its standard dimension, i.e. the number of independent parameters. When latent variables are present, however, the standard dimension might no longer be appropriate. Instead, an effective dimension should be used [5]. Zhang & Kočka [13] showed how to compute the effective dimensio

trigger

gout109

Mixed Influence Diagrams architecture is the first architecture for efficient exact solution of linear-quadratic conditional Gaussian influence diagrams with an additively decomposing utility function. The solution method as presented in this paper is based on the idea of lazy evaluation. The computational aspects of the a

scoliosis

Decision Making Based on Sampled Disease Occurrence in Animal Herdsprecise test and uncertainty introduced by the sampling have to be taken into account in order to act optimally. This paper formulates an influence diagram with discrete and continuous nodes to handle an example typical for animal production: a veterinarian who – as part of a biosecurity program – h