Solving real problems with Bayesian networks

Bayesian networks give a foundation for tools that support decision making based on evidence collected and the probabilities of one thing causing another (see “What are the chances of that?“).

COVID virus with DNA strand in background.
Image by Pete Linforth from Pixabay

The first algorithms that enabled Bayesian network models to be calculated on a computer were discovered separately by two different research groups in the late 1980s. Since then, a series of easy-to-use software packages have been developed that implement these algorithms, so that people without any knowledge of computing or statistics can easily build and run their own models.

These algorithms do ‘exact’ computations and can handle Bayesian networks for many different types of problems, but they can run into a barrier: when run on Bayesian networks beyond a certain size or complexity, they take too long to compute even on the world’s fastest computers. However, newer algorithms – which provide good approximate calculations rather than exact ones – have made it possible to deal with much larger problems, and this is a really exciting ongoing research area.

– Norman Fenton, Queen Mary University of London, Spring 2021

Download Issue 27 of the cs4fn magazine on Smart Health here.

This post and issue 27 of the cs4fn magazine have been funded by EPSRC as part of the PAMBAYESIAN project.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s