📄️ Discrete Network | Manual Construction
In this tutorial we will manually construct the simple discrete Bayesian network shown below.
📄️ Hybrid Network | Manual Construction
In this tutorial we will manually construct the Waste hybrid Bayesian network shown below. A hybrid network contains both Discrete and Continuous variables.
📄️ Parameter learning
In this tutorial we demonstrate the process of parameter learning, which uses data to determine the distribution(s) for one or more nodes in a Bayesian network.
📄️ Structural learning
In this tutorial we demonstrate the process of structural learning, which uses data to determine potential links for a Bayesian network.
📄️ Function Nodes
In this tutorial we will construct the example network Functions, included with Bayes Server, from scratch. Although not required for this tutorial, for reference the Functions network can be opened from the Start Page or from File/Open.
📄️ Causal AI | Optimization
In this tutorial we demonstrate the process of evidence optimization where some of the inputs are interventions.
📄️ Causal AI | Counterfactuals
In this tutorial we demonstrate the process of Counterfactual analysis. Counterfactual analysis can also be though of as Causal What If? analysis.