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Belief Network (belief + network)

Distribution by Scientific Domains

Information Science and Computing	40%

Selected Abstracts

BBN based approach for improving the software development process of an SME,a case study

JOURNAL OF SOFTWARE MAINTENANCE AND EVOLUTION: RESEARCH AND PRACTICE, Issue 2 2010
S. Bibi
Abstract This article proposes an approach for improving the software process of a small/medium company. The methodology is presented through a case study during which estimation models have been applied, evaluated and introduced in a telecommunication software development process. The proposed methodology uses Bayesian Belief Networks to represent the relationships among implementation, product and process metrics and their impact on the development effort. The estimation models that were derived were applied and evaluated on the on-going projects of the company. Finally, by performing the same analysis on data from the International Software Benchmarking Standards Group (ISBSG) repository, it is demonstrated how one company can utilize data from other companies when it lacks sufficient data of its own. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Conditional phase-type distributions for modelling patient length of stay in hospital

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, Issue 6 2003
A. H. Marshall
Abstract The proportion of elderly in the population is continuing to increase, placing additional demands on highly competitive medical budgets. The management of the care of the elderly within hospitals can be assisted by the accurate modelling of the length of stay of patients in hospital. This paper uses conditional phase-type distributions for modelling the length of stay of a group of elderly patients in hospital. The model incorporates the use of Bayesian belief networks with Coxian phase-type distributions, a special type of Markov model that describes the duration of stay in hospital as a process consisting of a sequence of latent phases. The incorporation of the Bayesian belief network in the model permits the inclusion of additional patient information which may provide a better understanding of the system, in particular the incorporation of any potential causal information that may exist in the data. [source]

An ecosystem-scale predictive model of coastal seagrass distribution

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS, Issue 4 2010
A. Grech
Abstract 1.Maintaining ecological processes that underpin the functioning of marine ecosystems requires planning and management of marine resources at an appropriate spatial scale. 2.The Great Barrier Reef World Heritage Area (GBR) is the world's largest World Heritage Area (approximately 348,000,km2) and second largest marine protected area. It is difficult to inform the planning and management of marine ecosystems at that scale because of the high cost associated with collecting data. To address this and to inform the management of coastal (approximately 15,m below mean sea level) habitats at the scale of the GBR, this study determined the presence and distribution of seagrass by generating a Geographic Information System (GIS)-based habitat suitability model. 3.A Bayesian belief network was used to quantify the relationship (dependencies) between seagrass and eight environmental drivers: relative wave exposure, bathymetry, spatial extent of flood plumes, season, substrate, region, tidal range and sea surface temperature. The analysis showed at the scale of the entire coastal GBR that the main drivers of seagrass presence were tidal range and relative wave exposure. Outputs of the model include probabilistic GIS-surfaces of seagrass habitat suitability in two seasons and at a planning unit of cell size 2,km×2,km. 4.The habitat suitability maps developed in this study extend along the entire GBR coast, and can inform the management of coastal seagrasses at an ecosystem scale. The predictive modelling approach addresses the problems associated with delineating habitats at the scale appropriate for the management of ecosystems and the cost of collecting field data. Copyright © 2010 John Wiley & Sons, Ltd. [source]

Toward better scoring metrics for pseudo-independent models

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 8 2004
Y. Xiang
Learning belief networks from data is NP-hard in general. A common method used in heuristic learning is the single-link lookahead search. When the problem domain is pseudo-independent (PI), the method cannot discover the underlying probabilistic model. In learning these models, to explicitly trade model accuracy and model complexity, parameterization of PI models is necessary. Understanding of PI models also provides a new dimension of trade-off in learning even when the underlying model may not be PI. In this work, we adopt a hypercube perspective to analyze PI models and derive an improved result for computing the maximum number of parameters needed to specify a full PI model. We also present results on parameterization of a subclass of partial PI models. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 749,768, 2004. [source]