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Introduction to Bayesian Networks

This self-paced Bayesian Networks course provides a comprehensive introduction to the theory and practical applications of this powerful tool.

This course will equip you with the skills to model and reason about uncertain systems. It is suitable whether you’re a beginner or have some existing statistical knowledge. While a basic understanding of statistics is helpful, it’s not required. The course is designed to be accessible to learners with varying levels of prior knowledge.

Starting with an introduction to probability theory, including probability distributions, conditional probability, and independence. You’ll then learn how to construct Bayesian networks, representing complex relationships using nodes, edges, and Conditional Probability Tables (CPTs).

By the end of this course, you’ll have the skills needed to effectively use Bayesian networks. You will gain the knowledge necessary for modelling and reasoning about uncertainty in systems.

This is a self-paced course, allowing you to move freely between modules. The duration time set is 10 hours, which is for guidance only, as you can go at your own pace.

Meet the Experts

Dr Roberto Puch-Solis is a Forensic Statistician at the Leverhulme Research Centre for Forensic Science (LRCFS).  

Roberto holds a PhD in Statistics from the University of Warwick and has specialized in forensic statistics since 2003. His research spans fibers, fingerprints, DNA, and the development of systems for reporting DNA evidence in court. Presently, he focuses on applying statistics and machine learning to forensic evidence, including DNA and firearms identification.

Dr Joyce Klu was recently a Lecturer in Forensic Statistics at the Leverhulme Centre for Forensic Science (LRCFS).  

Joyce holds an MSc and DPhil in Statistics from the University of Oxford. Before joining LRCFS, she worked as a Trial Medical Statistician at Oxford and a Research Assistant in Data Science at the University of Reading. During her time with LRCFS, Joyce’s research focused on improving statistical methods in forensic science, particularly in uncertainty analysis and measurement quantification.

FAQ’s

This course is ideal for:
  • Beginners with no prior experience in statistics
  • Learners with some statistical background
  • Professionals seeking to apply Bayesian reasoning in their work
Whether you’re just starting out or looking to deepen your understanding, this course is designed to be accessible to all.
You’ll gain a solid foundation in:
  • Probability theory: distributions, conditional probability, independence
  • Constructing Bayesian networks using nodes, edges, and Conditional Probability Tables (CPTs)
  • Applying Bayesian networks to model and reason about uncertainty in complex systems
This is an on-demand, fully online, self-paced course, meaning you can access the course when ever you want and where ever you want. You can study at your own pace and learn around your own schedule. It includes:
  • Interactive learning modules
  • Visual explanations and examples
  • Quizzes and exercises to reinforce your understanding
While the estimated duration is 10 hours, you can take as much time as you need. Feel free to move between modules at your own pace.
A basic understanding of statistics can help, but it’s not required. The course begins with an introduction to probability theory to ensure all learners can follow along.
You only need device with internet access and a modern web browser. All course materials and tools are provided within the platform.
Yes! You can revisit any module at any time. The course is designed to support flexible learning.
On successful completion of this course you will receive a downloadable certificate that will indicate the time and date of completion and hours spent. You will also have the opportunity to collect digital badges that you can share to your social media account.
By the end of the course, you’ll be able to:
  • Model uncertain systems using Bayesian networks
  • Analyse complex relationships and dependencies
  • Make informed decisions using probabilistic reasoning
Contact Subject Matter Experts or Technical Support on cpdinfo@dundee.ac.uk

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