Bayesian network

Submitted by Sukaina Bharwani | published 25th Mar 2011 | last updated 30th Mar 2011

Bayesian belief networks (Bns)

A Bn, is a type of decision support system based on probability theory which implements Bayes rule of probability. This rule shows mathematically how existing beliefs can be modified with the input of new evidence. Bns organise the body of knowledge in a given area by mapping out relationships among key variables and encoding them with numbers that represent the extent to which one variable is likely to affect another.

Bns have gained a reputation for being powerful techniques to model complex problems involving uncertain knowledge and uncertain impacts of causes. Ideally, Bns are a technique to assist decision-making that is especially helpful when there is scarcity and uncertainty in the data used in taking the decision and the factors are highly interlinked, all of which makes the problem highly complex. The graphical nature of Bns facilitates formal discussion of the structure of the proposed model and the ability of a Bn to describe the uncertain relationships between variables is ideal to describe the relationship between events, which may not be well understood.

Excerpt from paper "Tools in NeWater for participatory integrated assessment and adaptive management" 2007 H.J. Henriksen, P. Rasmussen, J. Bromley, A. de la Hera, and M.R. Llamas

See also Wikipedia's page on Bayesian Networks

Objectives

Bayesian belief networks (Bns) are proposed as one possible tool to be developed as an aid to stakeholder participation in integrated assessment, being a suitable tool for dialogue and gap analyses and thus for participatory integrated assessment (PIA).