4,872 research outputs found
A computational tool for Bayesian networks enhanced with reliability methods
A computational framework for the reduction and computation of Bayesian Networks enhanced with structural reliability methods is presented. During the last decades, the inner flexibility of the Bayesian Network method, its intuitive graphical structure and the strong mathematical background have attracted increasing interest in a large variety of applications involving joint probability of complex events and dependencies. Furthermore, the fast growing availability of computational power on the one side and the implementation of robust inference algorithms on the other, have additionally promoted the success of this method. Inference in Bayesian Networks is limited to only discrete variables (with the only exception of Gaussian distributions) in case of exact algorithms, whereas approximate approach allows to handle continuous distributions but can either result computationally inefficient or have unknown rates of convergence. This work provides a valid alternative to the traditional approach without renouncing to the reliability and robustness of exact inference computation. The methodology adopted is based on the combination of Bayesian Networks with structural reliability methods and allows to integrate random and interval variables within the Bayesian Network framework in the so called Enhanced Bayesian Networks. In the following, the computational algorithms developed are described and a simple structural application is proposed in order to fully show the capability of the tool developed
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Formalising Engineering Judgement on Software Dependability via Belief Networks
Biomedical applications of belief networks
Biomedicine is an area in which computers have long been expected to play a significant
role. Although many of the early claims have proved unrealistic, computers are gradually
becoming accepted in the biomedical, clinical and research environment. Within these
application areas, expert systems appear to have met with the most resistance, especially
when applied to image interpretation.In order to improve the acceptance of computerised decision support systems it is
necessary to provide the information needed to make rational judgements concerning
the inferences the system has made. This entails an explanation of what inferences
were made, how the inferences were made and how the results of the inference are to
be interpreted. Furthermore there must be a consistent approach to the combining of
information from low level computational processes through to high level expert analyses.nformation from low level computational processes through to high level expert analyses.
Until recently ad hoc formalisms were seen as the only tractable approach to reasoning
under uncertainty. A review of some of these formalisms suggests that they are less
than ideal for the purposes of decision making. Belief networks provide a tractable way
of utilising probability theory as an inference formalism by combining the theoretical
consistency of probability for inference and decision making, with the ability to use the
knowledge of domain experts.nowledge of domain experts.
The potential of belief networks in biomedical applications has already been recog¬
nised and there has been substantial research into the use of belief networks for medical
diagnosis and methods for handling large, interconnected networks. In this thesis the use
of belief networks is extended to include detailed image model matching to show how,
in principle, feature measurement can be undertaken in a fully probabilistic way. The
belief networks employed are usually cyclic and have strong influences between adjacent
nodes, so new techniques for probabilistic updating based on a model of the matching
process have been developed.An object-orientated inference shell called FLAPNet has been implemented and used
to apply the belief network formalism to two application domains. The first application is
model-based matching in fetal ultrasound images. The imaging modality and biological
variation in the subject make model matching a highly uncertain process. A dynamic,
deformable model, similar to active contour models, is used. A belief network combines
constraints derived from local evidence in the image, with global constraints derived from
trained models, to control the iterative refinement of an initial model cue.In the second application a belief network is used for the incremental aggregation of
evidence occurring during the classification of objects on a cervical smear slide as part of
an automated pre-screening system. A belief network provides both an explicit domain
model and a mechanism for the incremental aggregation of evidence, two attributes
important in pre-screening systems.Overall it is argued that belief networks combine the necessary quantitative features
required of a decision support system with desirable qualitative features that will lead
to improved acceptability of expert systems in the biomedical domain
Uncertainty Management and Evidential Reasoning with Structured Knowledge
This research addresses two intensive computational problems of reasoning under uncertainty in artificial intelligence. The first problem is to study the strategy for belief propagation over networks. The second problem is to explore properties of operations which construe the behaviour of those factors in the networks. In the study of operations for computing belief combination over a network model, the computational characteristics of operations are modelled by a set of axioms which are in conformity with human inductive and deductive reasoning. According to different topological connection of networks, we investigate four types of operations. These operations successfully present desirable results in the face of dependent, less informative, and conflicting evidences. As the connections in networks are complex, there exists a number of possible ways for belief propagation. An efficient graph decomposition technique has been used which converts the complicated networks into simply connected ones. This strategy integrates the logic and probabilistic aspects inference, and by using the four types of operations for its computation it gains the advantage of better description of results (interval-valued representation) and less information needed. The performance of this proposed techniques can be seen in the example for assessing civil engineering structure damage and results are in tune with intuition of practicing civil engineers
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