Introduction to the Bayesian Tools

In Rational Will, there are two tools available for Bayesian Analysis. One is "Bayesian Network" and another is "Diachronic Interpretation", or in simple words, we call it just "Bayesian Inference".

Bayesian Network is a visual diagram-based tool where you can create various nodes representing random variables and connect them based on their probabilistic dependencies. And in the regular Bayesian Inference tool, you can define a set of hypotheses. Then you can systematically add various experiments and observations, and then you can set the likelihood of those observations one by one. Finally, the beliefs of those hypotheses are calculated. So, this Bayesian Inference tool lets you collect evidence one by one and let you update your beliefs as the evidence arrives.

Here is a screenshot of a typical Bayesian Network model.

Rational Will Bayesian Network tool with the classic Sprinkler-Rain-Grass example: three nodes with marginal probabilities (Sprinkler 0.322, Rain 0.2, Grass wet 0.448), the conditional probability table for Grass wet, and a marginal probability bar chart for the Grass wet hypothesis.

And here is a screenshot of a typical Bayesian Inference model.

Rational Will Bayesian Inference tool with a medical diagnosis example: two competing hypotheses (Peptic Ulcer and Viral Gastroenteritis), Nausea and Vomiting as experiments/observations, a Causal Discovery panel and Updated Beliefs and Belief History charts.

To learn more about these tools, click the corresponding links from the following list.

Last updated on Jan 7, 2026

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Getting Started with the Bayesian Network