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Study our Samples Collection
On this page you will find a collection of constructed Hugin Knowledge Bases. The samples describe in detail numerous situations where Hugin Knowledge Bases have been developed using Bayesian network and influence diagram technology.
Download Developed Hugin Knowledge Bases (Networks)
For all the following samples there is a downloadable knowledge base supporting the description. Most of the knowledge bases have been installed on your computer with the Hugin software (All versions). You can find the networks in the Samples subdirectory of your Hugin installation.
Oil Wildcatter
By: Hugin Expert
In the oil wildcatter example, an influence diagram is constructed as an aid for an oil wildcatter to decide whether to drill for oil at a location where he has the opportunity to get information from seismic soundings. This example covers the problem of having more than one decision node in an influence diagram.
Chest Clinic (Asia)
By: Lauritzen & Spiegelhalter, 1988
Asia is a small Bayesian network that calculates the probability of a patient having tuberculosis, lung cancer or bronchitis respectively based on different factors - for example whether or not the patient has been to Asia recently.
Competitive Analysis
By: Professor Philippe Baumard
This example shows two Bayesian networks that are part of a series of 30 Strategic Analysis Bayesian networks developed for teaching MBA students competitive analysis at New York University Stern School of Business.
"Competitive Asymmetries" assesses the threat of an anti-trust case based on industry structure and the firm's position in this industry.
"Threat of Entry" assesses the probable entry strategy of a new entrant, based on the industry characteristics.
Stud Farm
By: Hugin Expert
In the stud farm example, a Bayesian network is used to calculate the probabilities of the horses in a stud farm being carriers of a recessive gene causing a life threatening disease.
The Monty Hall Puzzle
By: Hugin Expert
The Monty Hall example demonstrates the use of a small Bayesian network to solve the Monty Hall Puzzle.
Think-Box
By: Michael Höhle
Think-box gives a good and thorough description of how you can get from a description of a simple game of dice to an influence diagram that can help find a winning strategy.
A Simplified Poker Game
By: Finn V. Jensen
Poker is a Bayesian network that models a simplified version of the game of poker. The network can help predict who has the best hand — you or your opponent.
Mildew
By: Finn V. Jensen
The Mildew examples are four different influence diagrams modeling the situation where a farmer has to decide on a treatment with fungicides for a wheat field. The influence diagrams model different scenarios depending on the information available.