In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data—that is, in the presence of missing values or hidden variables. In a recent paper, I introduced an algorithm called Structural EM that combines the standard Expectation Maximization (EM)...

This report is for the literature concerning Bayesian Networks, which will be applied to our collaboration project with ACT Planning and Land Authority (ACT-PLA) involving population prediction, suburb development potential, and land supply strategy, etc. Bayesian Network graphically represents a problem domain and is able to integrate multiply data sources as well as conduct bi-directional inf...

Belief fusion is the principle of combining separate beliefs or bodies of evidence originating from different sources. Depending on the situation to be modelled, different belief fusion methods can be applied. Cumulative and averaging belief fusion is defined for fusing opinions in subjective logic, and for fusing belief functions in general. The principle of unfusion is the opposite of fusion,...

50 AI MAGAZINE u n d e r s t a n d i n g (Charniak and Goldman 1989a, 1989b; Goldman 1990), vision (Levitt, Mullin, and Binford 1989), heuristic search (Hansson and Mayer 1989), and so on. It is probably fair to say that Bayesian networks are to a large segment of the AI-uncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be the...

Belief fusion is the principle of combining separate beliefs or bodies of evidence originating from different sources. Depending on the situation to be modelled, different belief fusion methods can be applied. Cumulative and averaging belief fusion is defined for fusing opinions in subjective logic, and for fusing belief functions in general. The principle of fission is the opposite of fusion, ...

Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in the late 1970’s to model the distributed processing in reading comprehension. Since then they have attracted much attention and have become popular within the AI probability and uncertainty community. As a natural and efficient model for human’s inferential reasoning, belief networks have emerged ...

The event bush is a new formalism for organizing knowledge in various fields of geoscience, particularly suitable for hazard assessment purposes. Acting as an intermediary between expert knowledge and the well-established field of Bayesian belief networks, the event bush allows at the same time a variety of other applications, linking geoscientific knowledge to the field of artificial intellige...

Designers of dependable systems need to present assurance cases that support the claims made about the system’s dependability. Building this assurance case, incorporating different types of evidence and reasoning, can be daunting. In this paper we argue that, thanks to their flexibility and expressive capabilities, Bayesian Belief Networks are particularly suitable for building such assurance c...