Getting started

Bayes Server is a tool for modeling Bayesian networks, Dynamic Bayesian networks, Causal models and Decision graphs.

Bayesian networks are widely used in the fields of Artificial Intelligence, Machine Learning, Data Science, Big data, and Time Series Analysis.

Some examples of how Bayesian networks are used are given below:

When new to Bayes Server, the following tutorials are recommended:


The following tutorials provide step by step guides to building Bayesian networks and dynamic Bayesian networks.

Bayesian networks

Bayesian networks are a graphical approach to modeling, using probability. In a network, nodes are used to represent variables, and links to indicate that one node influences another. This allows the relationship between variables to be visualized easily. Each node in a Bayesian network requires a probability distribution to be specified (conditional on its parents), and Bayes Server uses advanced algorithms to combine these distributions, in order to answer queries (questions/predictions).

More information about Bayesian networks.

Dynamic Bayesian networks

Dynamic Bayesian networks add the concept of time, to allow for time series/sequence modeling.

More information about dynamic Bayesian networks.

Decision graphs

Decision graphs add the concept of utilities (e.g. profits & costs) and decisions, to allow for decision making under uncertainty.

More information about Decision graphs.