In this study three commonly used computer programs for influence diagram evaluation are compared: Netica, Hugin Expert and PrecisionTree. The programs are analysed with respect to three main issues: how they comply with semantic rules for influence diagrams, how the issue of asymmetric decision problems is handled in each tool, and if and in what way they implement the arc reversal functionali...

Advancing stakeholder participation beyond consultation offers a range of benefits for local flood risk management, particularly as responsibilities are increasingly devolved to local levels. This paper details the design and implementation of a participatory approach to identify intervention options for managing local flood risk. Within this approach, Bayesian networks were used to generate a ...

Bayesian network is a powerful tool to represent and deal with uncertain knowledge. This paper mainly introduces some technologies and methods of modeling Bayesian network, which are used in the building Maize Diseases Diagnosis system. In the construction of Bayesian network, noisy-or model and transformation from certainty factor to probability are used. Then maize disease diagnosis system ba...

Bayesian networks are rapidly becoming a tool of choice for applied Artificial Intelligence. There have been many medical applications of BNs however few applying data mining methods to epidemiology. In a previous study we looked at such an application to epidemiological data, specifically assessment of risk for coronary heart disease. In that previous study, we featured two Bayesian networks “...

evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. in this paper, bayesian belief networks (bbns) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. as a case study, we estimated the surface water evaporation of the persian gulf and worked with a dataset of observations (dai...

In educational assessment, as in many other areas of application for Bayesian networks, most variables are ordinal. Additionally conditional probability tables need to express monotonic relationships; e.g., increasing skill should mean increasing chance of a better performances on an assessment task. This paper describes a flexible parameterization for conditional probability tables based on it...

A Bayesian network, or directed acyclic graphical model is a probabilistic graphical model that represents conditional dependencies and conditional independencies of a set of random variables. Each node is associated with a probability function that takes as input a particular set of values for the node’s parent variables and gives the probability of the variable represented by the node, condit...

Resumen. Diversos problemas del mundo real pueden ser modelados a través de las redes bayesianas. Este trabajo presenta el desarrollo de una red bayesiana clasificadora para aplicaciones médicas, dicha propuesta es un algoritmo hibrido combinado con algoritmos genéticos. En este art́ıculo utilizamos una base de datos de 267 conjuntos de imágenes SPECT descargadas de SPECT Heart Data Set. Estos d...

Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...