Bayesian Network (Interactive)
Create a small directed acyclic graph (DAG) of binary variables, specify conditional probability tables, and run exact inference with evidence — right in the browser.
Note: this tool is for learning. For large models use a specialized engine.
1. Nodes
All nodes are boolean: True / False.
3. Inference
Choose a query variable and (optional) evidence. We'll compute P(Query = True | Evidence).
Result:
—
Bayesian network recap
A Bayesian network is a compact way to encode a joint probability distribution using conditional independencies. Formally, for nodes \( X_1, \dots, X_n \) in a DAG, the joint factorises as:
P(X_1, \dots, X_n) = \prod_{i=1}^n P(X_i \mid \text{Parents}(X_i))
Inference answers questions like \( P(\text{Disease} \mid \text{Test} = +) \). Our tool uses enumeration: it sums over all hidden variables to compute the normalizing constant.
Tips for using this page
- Keep to 5–6 variables to avoid exponential blowup.
- Set every CPT row, or inference will assume 0.5.
- Use the evidence toggles to test diagnostic vs causal queries.
Formula (LaTeX) + variables + units
This section shows the formulas used by the calculator engine, plus variable definitions and units.
Formula (extracted LaTeX)
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Formula (extracted text)
P(X_1, \dots, X_n) = \prod_{i=1}^n P(X_i \mid \text{Parents}(X_i))
Variables and units
- No variables provided in audit spec.
Sources (authoritative):
- NIST — Weights and measures — nist.gov · Accessed 2026-01-19
https://www.nist.gov/pml/weights-and-measures - FTC — Consumer advice — consumer.ftc.gov · Accessed 2026-01-19
https://consumer.ftc.gov/
Changelog
Version: 0.1.0-draft
Last code update: 2026-01-19
Last code update: 2026-01-19
0.1.0-draft · 2026-01-19
- Initial audit spec draft generated from HTML extraction (review required).
- Verify formulas match the calculator engine and convert any text-only formulas to LaTeX.
- Confirm sources are authoritative and relevant to the calculator methodology.