Risk identification, impact assessment, and response planning constitute three building blocks of project risk management. Correspondingly, three types of interactions could be envisioned between risks, between impacts of several risks on a portfolio component, and between several responses. While the interdependency of risks is a well-recognized issue, the other two types of interactions remai...

This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (DBNs) were evaluated. The work proposed is divided into three stages, namely preprocessing, feature extraction and classification. Preprocessing includes baseline estimation and normalization as well as segmentation...

Inferring Dynamic Bayesian Networks (DBNs) from multivariate time series data is a key step towards the understanding of complex systems as it reveals important dependency relationship underlying such systems. Most of the traditional approaches assume a “static” DBN. Yet in many relevant applications, such as those arising in biology and social sciences, the dependency structures may vary over ...

In the mid-1980s a new trend in probabilistic reasoning with uncertainty in knowledge-based systems became discernable taking a graphical representation of knowledge as a point of departure. We use the phrase network models to denote this type of model In the preceding sections, we have concentrated primarily on models for plausible reasoning that were developed especially for knowledge-based s...

In this paper, we merge the parametric structure of neural networks into a segmental semi-Markov model to set up a Bayesian framework for protein structure prediction. The parametric model, which can also be regarded as an extension of a sigmoid belief network, captures the underlying dependency in residue sequences. The results of numerical experiments indicate the usefulness of this approach.

A participatory integrated (social, economic, environmental) approach based on causal loop diagram, Bayesian belief networks and evolutionary multiobjective optimisation is proposed for efficient water resources management. The proposed methodology incorporates all the conflicting objectives in the decision making process. Causal loop diagram allows a range of different factors to be considered...