The ABNMS BN Repository
60 BNs found.
This BN is an example of dynamic Bayesian network which includes feedback loops. The feedback loop represents the commonly accepted feedback relationship between socioeconomic status and education.
An assessment of the expected value of putting in a fence to promote plant survival, in the face of factors that affect the durability of the fence.
An example decision network for the dilemma of whether or not one should go surfing. The expected value of heading into the surf (or remaining put) is dependent on the wave quality, which is in turn dependent on wind direction and swell size. Both the wind direction and the swell size can be (imperfectly) forecast, and examples of handling these imperfect forecasts are included in the network.
An extremely simple 2-node example demonstrating how true positive/false positive cases can be handled, in this case as applied to a steroid use test.
An example of causal discovery where it can falsify the one causal pattern or the other in the Popperian language through its patterns. The BN learns three chains partially and the design can then be scaled up.
An example of using CPT equations in Netica. The main equation is very simple and updates the final position given the initial position, speed and time.
A decision network which decides if an Amniocentesis test provides a probability of a positive or negative result given the mother's age. It also provides the probability of a miscarriage should the test not be carried out appropriately. Amniocentesis is a test that can be done on pregnant women to test for certain foetal abnormalities, such as Down’s Syndrome.
This BN calculates the probability of native fish abundance given pesticide in use and in river, drought conditions, river flow, annual rainfall and tree conditions.
The Decision Graph is applied for the assessment and optimization of an existing threshold-based debris flow warning system. To model the warning system and compute the technical and inherent reliability, the Bayesian Network, which is the Decision Graph without the utility node, can be applied alone. Paper: <www.era.bgu.tum.de...>.
An extremely simple medical diagnosis network example.
The model examines the role of the landscape, fuel load, fuel moisture and fuel breaks on changing the risk to property in San Diego County, USA.
An SR latch is one of the simplest kinds of electronic memory that can be built with logic gates. See <en.wikipedia.org...> for more information.
Set the evidence for 'R' (Reset) and 'S' (Set) to 'False' for all time steps. Then enter a 'True' in 'S' (Set) for time step 3. The 'output' of NOR1 will be locked to True thereafter. (You can then set 'R' to 'True' in a later timestep to clear it.) Requires GeNIe.
This is a BN learned by CaMML using the UC Irvine auto-mpg data set; citing Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [<archive.ics.uci.edu...>]. Irvine, CA: University of California, School of Information and Computer Science.
A network that uses meteorological data with expert judgements to forecast weather in Northeastern Colorado.
A system used to model waste water treatment and management
A network modelling symptoms of disease and birth asphyxia in a child.
A preliminary model for the production of beer from Danish malting barley grown without pesticides.
This network represents a probabilistic model for diagnosis based on medical knowledge.
Sample network showing probability of lung cancer given certain factors, alongside some symptoms.