The BNMA BN Repository

Provides risk ratings for prioritizing which U.S. National Archives and Records Administration records to preserve and archive, for 35mm B&W films.

In our recently published article “A scenario study of the acceptability to ocean users of more and less invasive management after shark-human interactions”, Bayesian Network (BN) was chosen as the principal analysis tool for identifying patterns from a study of 1769 valid responses from a survey which was open to all Australian residents aged 18 or over. The study was aimed to (1) Improve understanding of community attitudes in NSW to shark management options, and (2) Improve understanding of contextual influences on attitudes to shark bite mitigation approaches and how authorities should respond following an interaction. A modified BN model (26 nodes, 54 links) was proposed (implemented in Netica version 6.09) to improve the prediction performance of those four variables of risk management policy/strategy in the original BN model (26 nodes, 44 links) (implemented in Netica version 6.05)

BAIPA (for “Biocontrol Adverse Impact Probability Assessment”) is a new probabilistic tool that assesses the potential unwanted impact of organisms released in biological control programmes on non-target species.

The BN integrates information on the potential of an insect parasitoid used in biological control to: (1) disperse in new habitats; (2) interact with non-target species; and eventually (3) negatively impact the populations of the non-target species.

The two exemplar BNs on this repository assess the potential of an insect parasitoid released for biological control of pasture weevils in New Zealand (Microctonus aethiopoides) to disperse in new habitats, interact with on-target species (native weevils in the genus Nicaeana) , and eventually negatively impact the populations of the non-target species. Two habitats are assessed, low-intensity grazing pastures (LIP) and native grassland (NG - this model). Both models were built with GeNIe <bayesfusion.co...>

BAIPA (for “Biocontrol Adverse Impact Probability Assessment”) is a new probabilistic tool that assesses the potential unwanted impact of organisms released in biological control programmes on non-target species.

The BN integrates information on the potential of an insect parasitoid used in biological control to: (1) disperse in new habitats; (2) interact with non-target species; and eventually (3) negatively impact the populations of the non-target species.

The two exemplar BNs on this repository assess the potential of an insect parasitoid released for biological control of pasture weevils in New Zealand (Microctonus aethiopoides) to disperse in new habitats, interact with on-target species (native weevils in the genus Nicaeana) , and eventually negatively impact the populations of the non-target species. Two habitats are assessed, low-intensity grazing pastures (LIP - this model) and native grassland (NG). Both models were built with GeNIe <bayesfusion.co...>

This example shows that introducing a common cause can make an important change, even if the maringal probabilities of the children remain the same.

Catastrophic failure occurs only if all 3 subsystems fail.

The probability of Catastrophic_Failure1 (with no findings) is 2.9791e-011, which may be an acceptable risk.

However, more careful modeling leads to the addition of the Incorrect_Test_Procedure node. With only this change, the failure probability jumps to 0.003%, which is unacceptable.

Notice that the failure probability of the subsystems remains 0.031% in both cases, and the CPT of Catastrophic_Failure remains unchanged.

BN link: <www.norsys.com...>

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.

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.