Bayesian Networks - a powerful alternative to traditional machine learning models

The quickest way to learn about Bayesian Networks?
Join our course on August 24th.



Limitations of traditional methods hinder impactful business decisions


Machine learning and data analytics offer a unique opportunity to guide our business decisions to be as impactful as possible, whether addressing churn, optimizing prices or preventing fraud.  However, there are many cases where traditional modeling techniques do not offer the desired guidance. Firstly, they are able to predict whether something will happen but not why something will happen. Secondly, the more advanced the methods become, the more difficult it is to explain them to business users. In the worst-case scenario, you end up with a black box model that no one wants to adopt. Thirdly, traditional machine learning models can only consider the available data, and will not take into account external input from domain experts.

Take customer churn, which has a big impact on company profitability. To really tackle churn, we need to understand why a customer has the intention of leaving. Often it’s not the result of a single event but a combination of factors influencing the customer experience. Only through understanding this process, are we able to properly address churn and approach customers with a message that is suitable for their specific situation.

We want to introduce you to a powerful alternative, rarely used in business, that can help you obtain these insights and overcome the limitations of traditional machine learning techniques.


Bayesian Networks are a powerful tool to visualize and model complex relationships


Where traditional techniques fail, Bayesian Networks excel. This method has already proven its value in the medical field, with a wide range of applications to model the complexity of the human body. Recently Bayesian Networks have started to gain traction in business as well. To name a few use cases, Bayesian Networks have been applied to: explain customer behavior and customer satisfaction, optimize predictive machine maintenance, model customer churn, and detect fraudulent transactions.

The reasons for this increased interest are apparent. Firstly, Bayesian Networks have the ability to visualize complex relationships and effects using a so-called Directed Acyclic Graph; in other words a causal network. This allows for easy interpretation, while also indicating the causal role of specific factors. For decision-makers, such insights are invaluable. Secondly, Bayesian Networks can incorporate domain knowledge using priors. Via priors, existing expertise can be integrated into the model to boost performance and more accurately represent reality. Thirdly, Bayesian Networks can provide more robust results due to their inherent ability to deal with uncertainty and incomplete data.


The Bayesian Networks training is your stepping-stone for applying this method in business


During our 1-day training, you gain both a theoretical and practical understanding of Bayesian Networks. We start by covering the mathematical foundation of the Bayes’ theorem and offer you three different Bayesian Network techniques. You will be challenged to implement these innovative methods in a real-life programming case. Particular focus is placed on the convenient explainability of Bayesian Networks and the translation to convincing business advice. Providing the ideal preparation to start using this powerful method in your business.


Sign up to the course

AI quick scan

Test how your company is performing on developing and implementing AI solutions and platform capabilities:


1.My organisation communicates a clear end-state vision on how AI can transform our business
2.AI initiatives are aligned with the strategic goals of my organisation
3.My organisation has a roadmap that focuses data analytics efforts on large scale business opportunities
4.My organisation succesfully builds high potential proof of concepts with AI
5.AI use cases are developed end-to-end from data to algorithm to an application, such that users can interact with the algorithm
6.AI use cases are automated and operationalized in a production environment
7.AI use cases are fully adopted by the business, without unintended retreat to own judgement
8.AI use cases change the business process fundamentally if adopted
9.AI use case MVP's are continuously being improved, leaving MVP state
10.My organisation succesfully scales impact of AI use cases
11.There is an infrastructure in place for users to approach (a selection of) data
12.There is a possibility to develop models, for example in sandbox like environments
13.Algorithms are operationalized and automatically predict on new data
14.Your organisation has deployed one ore more experiments with modern, for example cloud-based, infrastructure
15.AI platform standards are set and managed
16.Clarity exists on target infrastructure for new solutions
17.Existence of legacy is accepted as a given, but migrations and dependencies are managed with a impact driven mindset
18.AI solutions are in production on scalable modern infrastructure
19.There are standardized operational processes to develop, deploy and maintain models (model management) in modern infrastructure.
20.Users enjoy flexibility to access any data and use it to build solutions with diverse requirements, while still maintaining certain standards managed centrally