Population projection for many developing countries could be quite a challenging task for the demographers mostly due to lack of availability of enough reliable data. The objective of this paper is to present an overview of the existing methods for population forecasting and to propose an alternative based on the Bayesian statistics, combining the formality of inference. The analysis has been m...

In many areas of application it is important to estimate unknown model parameters in order to model precisely the underlying dynamics of a physical system. In this context the Bayesian approach is a powerful tool to combine observed data along with prior knowledge to gain a current (probabilistic) understanding of unknown model parameters. We have applied the methodology combining Bayesian infe...

Region-based image segmentation methods require some criterion for determining when to merge regions. This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context. Our approach does not require parameter estimation , and is therefore particularly beneecial for cases in which estimation-based methods are most prone to error: when litt...

This paper applies Bayesian Networks to support information extraction from unstructured, ungrammatical, and incoherent data sources for semantic annotation. A tool has been developed that combines ontologies, machine learning, and information extraction and probabilistic reasoning techniques to support the extraction process. Data acquisition is performed with the aid of knowledge specified in...

We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence structures, as present in infrastructure and structural systems, and they fac...

We propose and demonstrate a methodology for building tractable normative intelligent tutoring systems (ITSs). A normative ITS uses a Bayesian network for long-term student modelling and decision theory to select the next tutorial action. Because normative theories are a general framework for rational behaviour, they can be used to both define and apply learning theories in a rational, and ther...

Bei der Fusion heterogener Informationsquellen muss deren unterschiedlicher Abstraktionsgrad und deren unterschiedliche Natur (Formalisierung) überwunden werden. Essenzielle Forderungen an eine Fusionsmethodik sind die Fähigkeiten zur Transformation, Fusion und Fokussierung. Es wird ge-zeigt, dass die Bayes'sche Wahrscheinlichkeitstheorie in einer Degree-of-Belief-Deutung jede dieser Forderunge...

This paper presents a Bayesian methodology for computer-aided experimental design of heterogeneous scaffolds for tissue engineering applications. These heterogeneous scaffolds have spatial distributions of growth factors designed to induce and direct the growth of new tissue as the scaffolds degrade. While early scaffold designs have been essentially homogenous, new solid freeform fabrication (...

Most ITS have a means of providing assistance to the student, either on student request or when the tutor determines it would be effective. Presumably, such assistance is included by the ITS designers since they feel it benefits the students. However, whether—and how—help helps students has not been a well studied problem in the ITS community. In this paper we present three approaches for evalu...