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Bayesian Belief Networks

Aim of the resource

Allows the building of complex networks from simple segments and it enables uncertainties to be assessed at every stage, so the outcomes of the network reflect the weight of the evidence that supports the conclusion.

Using the resource
Requirements for using the resource:
<ul>
<li>The data requirements are basically linked to the structure and to the purpose of the BBN. All types of data can be used for developing a BBN, but internally the nodes will usually have a discrete set of states and the information about the links is held as a set of conditional probability tables.&nbsp;</li>
<li>For computational purposes, it is a good idea to limit the number of states in any node and to limit the number of links going into any one node.</li>
<li>The BBN structure can be set up purely from expert opinion, and that is probably the most likely route for most case studies. Most networks are then developed iteratively, and if the nodes change during these iterative steps then so will the data requirement</li>
</ul>
Potential benefits from using the resource
The facility of using a variety of information types including expert opinion, experience and historical data to manage and represent uncertainty.
It can also learn from new data by updating its probabilities and so it always reflects the current state of knowledge
The graphical interface provides an attractive way of presenting the arguments to stakeholders. It helps to focus ideas during network development and encourages transparency about the system structure
The graphical interface provides an attractive way of presenting the arguments to stakeholders. It helps to focus ideas during network development and encourages transparency about the system structure
Potential limitations from using the resource
The major constraint on any ecosystem services assessment is the availability of information (data, model simulations, etc.) at the correct spatial and temporal scales
The nodes in these networks usually operate with categorised rather than continuous variables. This is not normally a limitation to developing an effective network and there are functions to convert continuous measurements to the chosen discrete states
Feedbacks within any one time instance of the BBN are not allowed, but the use of time slicing (i.e. repeating the BBN at a series of points in time) can overcome some of this problem
Scope
Scale of application:
Global
Regional
Sub-regional
National
Subnational
Local
Practical information
UN languages in which the resource is available:
Development stage:
Full, working product
Contact details
HUGIN
Resources
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A Bayesian Belief Network is a framework that uses a graphical representation to show the flow of information in a system. It has nodes or vertices to represent variables which can include observed quantities, latent (unobserved) quantities, expert opinion, model outputs, or unknown parameters. There are links or edges joining parent nodes to child nodes. The difference between this and other similar frameworks is in the use of conditional probabilities to express the relationships between nodes.

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