Bayes theorem visualizer
Open Bayes theorem visualizer on Happytails.ai. This view has the same page content and links, with explicit tool capabilities. Read the processing notes before using a tool.
Tool capabilities
{
"id": "bayes-theorem-visualizer",
"category": "calculators",
"built": true,
"requirements": "Two mutually exclusive, exhaustive hypotheses. Enter probabilities from 0 to 100% and a reference total from 1 to one billion. Counts are expectations, not observations or rounded people. A posterior is undefined when its conditioning event has probability zero. This is a calculation from supplied assumptions, not a diagnosis or inferred likelihood.",
"implementation": {
"fields": [
{
"key": "prior",
"label": "Prior probability of A (%)",
"value": 10,
"type": "number",
"min": 0,
"max": 100,
"step": "any"
},
{
"key": "likelihood",
"label": "Probability of evidence given A (%)",
"value": 80,
"type": "number",
"min": 0,
"max": 100,
"step": "any"
},
{
"key": "alternative",
"label": "Probability of evidence given not A (%)",
"value": 20,
"type": "number",
"min": 0,
"max": 100,
"step": "any"
},
{
"key": "total",
"label": "Reference total for expected counts",
"value": 1000,
"type": "number",
"min": 1,
"max": 1000000000,
"step": 1
}
],
"input": false,
"live": true,
"presentation": "metrics",
"note": "State a prior and two likelihoods. The tree shows expected frequencies for your reference total; these are not real observations. A zero-probability conditioning event has no defined posterior.",
"experience": {
"automatic": true,
"single": false,
"inputMaxLength": 1000000,
"inputMode": "text",
"label": "Your text",
"placeholder": "Type or paste your input…",
"help": "Change the input or settings to update your result. Results update as you type. Up to 1,000,000 characters.",
"helpParts": [
"Change the input or settings to update your result.",
"Results update as you type.",
"Up to 1,000,000 characters."
],
"action": null
},
"mode": "automatic",
"interactive": false
},
"execution": "Browser interface",
"api": null,
"browser_agent": {
"supported": true,
"name": "run_current_tool",
"discovery": "WebMCP on the human page in a supporting browser",
"verification": "See audit/API-AUDIT.md; availability is not verification"
}
}Page guide
Update a prior from stated likelihoods and see every branch of the expected-frequency tree.
How to use Bayes theorem visualizer
- Enter the prior probability and the likelihood of the evidence under each hypothesis.
- Choose a reference total to see expected frequencies.
- Read the evidence denominator before using the posterior, and download the full tree.
Useful next steps
Read the model
Update a prior from stated likelihoods and see every branch of the expected-frequency tree.
Check the definition
Two mutually exclusive, exhaustive hypotheses. Enter probabilities from 0 to 100% and a reference total from 1 to one billion. Counts are expectations, not observations or rounded people. A posterior is undefined when its conditioning event has probability zero. This is a calculation from supplied assumptions, not a diagnosis or inferred likelihood.
Keep the calculation
Download SVG for the chart, JSON for unrounded values, CSV for the table and the assumptions note. Your input stays on your device.