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If you are interested in buying AgenaRisk please get in contact with us here.

If you have purchased a copy of Fenton and Neil's book you can download your copy of AgenaRisk Lite  here.

Fenton and Neil's book: "Risk Assessment with Bayesian Networks"

AgenaRisk users include:

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### Example Models

AgenaRisk comes with a large number of example models that illustrate the different kinds of problem that can be tackled.

The models are divided into the following categories: Introductory (i.e. used or referred to in the AgenaRisk Tutorials), Basic, Advanced. To view and run these models you will need to install the evaluation version of AgenaRisk. But we also have examples of models in action.

Many of the models are described in details in the book Fenton, NE and Neil M "Risk Assessment with Bayesian Networks" and the book website also has the most up to date set of models.

#### Introductory Models

 Name Description Asia Diagnoses tuberculosis, lung cancer and bronchitis based on various factors including whether the patient in question has been to Asia recently Car Costs Predicts the long-term running costs of owning different makes of car Flood Model A temporal model that illustrates how risk can be represented causally Hypothesis Testing Statistically tests the hypothesis that one material is more faulty than another

#### Basic Models

 Name Description Aggregating Distributions Calculates the total cost of a product based upon the uncertain costs of its components Causal Risk Register Determines whether a project is at risk by reasoning about the causes and consequences of risk Constraint Satisfaction Solves the classic "map colouring" problem, where a map has to be filled in with four colours in such a way that no adjacent region can contain the same clour Dependent Coin Flips Two simple probability experiments that involve flipping coins Fault Tree Analysis Two examples of how AgenaRisk can be used to construct a fault tree for analysing the reliability of complex systems Fire Models a simple fire alarm system and shows the principle of "explaining away" evidence Intensive Care Monitoring A model for monitoring patients in intensive care KUUUB Adjusts quantitative loss predictions using qualitative KUUUB (Known Unknown, Unknown Unknown and Bias) data Monty Hall Dilemma An illustration of the classic game show dilemma in which a contestant tries to find a prize by opening doors Mountain Pass Decision analysis example in which a man has to decide how he should travel to an appointment Naive Bayesian Classification Uses existing data about known banks to characterise and thus predict the loss distributions of unknown banks Noisy Or A simple model that shows the effects of liver failure and hepatitis on jaundice and that demonstrates the Noisy Or function Parameterised Distribution using Constants Illustrates how constants can be used to change the parameters of distributions without needing to edit the corresponding expression or regenerate the corresponding NPT Printer Fault Diagnosis A model for predicting and diagnosing printer faults Risk Control Self Assessment Predicts losses in a business based upon the presence and absence of different risk controls Risk Drivers and Indicators Two models that show the correlation between risk drivers and risk indicators River Flooding A time-series model showing how the flooding of a river is influenced by rainfall and the state of its flood defences Safety A model used for assessing the safety of a critical system Simple Testing Process Illustrates how the quality of the testing process can influence the number of defects found during testing Statistical Distributions Illustrates the range of different statistical distributions supported by AgenaRisk Wet Grass A simple example that shows how a single event can occur as a result of two different causes