Bayesian Network Software

Build Bayesian networks in a visual Windows desktop app. Work offline, enter observations and see beliefs update in real time, without code.

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Perpetual license Pay once, no subscription 128 citations In peer-reviewed research 100% offline Your data stays on your machine

A test result, a failed component, a witness statement.

Bayesian Doctor is built for the moment a belief has to move because evidence arrived, and the size of that move has to be defensible.

Build Bayesian Networks Visually - No Coding Required

Draw causal diagrams, define conditional probabilities and set hypotheses through a point-and-click graphical interface. Built for professionals and students who think visually, with a perpetual license and live human support.

Bayesian Doctor is a Bayesian network and Bayesian inference tool built on a directed graphical model. Draw your network with point-and-click ease, connect causes to effects, and diagnose the root cause of what you observe - no Python, R, or scripting needed.

Bayesian network sprinkler example modeled visually in Bayesian Doctor

Set an Observation, Watch Every Belief Update

When you observe the state of a variable, instantiate it with one click. For example, you observed that the grass is wet, so you set Grass = True - and the probability of Rain = True updates instantly across the whole network.

Instantiating an observed Bayesian network variable in Bayesian Doctor

Beyond True and False: Multi-State Variables

Variables are not limited to Yes/No or True/False states - model as many states as your problem needs. The conditional probability tables stay easy to read, and probabilities rescale automatically as you edit.

Multi-state random variable with its conditional probability table

Update Beliefs Over Time with Bayesian Inference

When you have many random variables to work with, a Bayesian network conditional probability table can become very complicated and fairly impractical for real-life diagnostics. Instead, incorporate one observation at a time and update your beliefs accordingly. This is where the diachronic interpretation comes into play.

“Diachronic” means something happening over time: when you receive new data, the probability of your hypotheses changes. Diachronic interpretation is a systematic way to update your beliefs as new data arrives. In a lab-like setting, add as many mutually exclusive and non-exclusive hypotheses as you want, add experiments and observations, and run a causal discovery session to find the probability of each hypothesis - useful for diagnostics from healthcare to everyday problems.

Bayesian inference updating hypothesis beliefs from new observations

Diagnose Complex Cases as Symptoms Arrive

An example of finding the cause of nausea and vomiting: the initial hypotheses were Peptic Ulcer and Viral Gastroenteritis. As each symptom arrives, diachronic interpretation updates the belief in every hypothesis, pointing you to the most likely cause.

Bayesian inference belief update while diagnosing competing hypotheses

Also Included in Rational Will, Our Full Decision Suite

This Bayesian network software (a.k.a. Bayesian Doctor) is also included in our composite product Rational Will®, alongside decision trees, multi-criteria analysis, and more - one streamlined experience for every decision modeling tool. If you get Rational Will, you will not need to acquire this software separately.

Cited in 128 peer-reviewed papers.
Used for coursework at 293 universities.

Researchers do not cite tools they cannot defend. When a method has to hold up under review, the software behind it gets scrutinized too.

Browse the research index

Ichlasul Amal, et al. Meningkatkan Kepatuhan Terhadap Standar Pakan Nasional: Pendekatan AHP Berbasis Pemangku Kepentingan untuk Peningkatan Kualitas Jagung di Sulawesi Selatan. Jurnal Ilmu Peternakan Halu Oleo, 2026. DOI
Jiyoung Shim. Exploring the Relative Importance of Reading Material Selection Factors Using AHP. Journal of the Korean Society for Information Management, 2026. DOI
T. G. Asresehegn, V. Valencia, S. Schulz, G. Woldewahid, G. Gebrehawariat, R. P. O. Schulte. Community responses to land degradation: Insights from land restoration bright-spot communities in the Ethiopian Highlands. Environmental Development, 2026. DOI

The three most recent entries from our research index.

University of North Carolina at Charlotte · University of Sharjah · University of manchester · Chulalongkorn University · Colorado State University · Institut Teknologi Bandung · Institut Teknologi Sepuluh Nopember · IPB UNIVERSITY · and 285 more.

Source: our student-license survey, 293 universities and counting.

Terms that have become rare.

Your data never leaves your machine.

Fully offline desktop software. Your models and results never touch a server, ours or anyone's. Nothing to upload, nothing to leak.

You buy it once. It is yours.

A true perpetual license, sold this way since 2007. No subscription meter running while you finish the work.

One-to-one support from a human domain expert.

Not a chatbot, not a call center. Support is a live conversation with the engineers who build the software. One call away, on Zoom or Teams, free.

Stuck at 11 p.m. with a model due Friday? Talk to our engineers.

Every trial and every license includes free live 1:1 sessions on Zoom or Teams. You will talk to a real human, a domain expert who builds this software, and we will help you model your actual problem. No chatbot, no call center, no ticket queue.

Weigh it yourself. The trial is the full product: free, offline, on your machine.