happytails.ai

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
  1. Enter the prior probability and the likelihood of the evidence under each hypothesis.
  2. Choose a reference total to see expected frequencies.
  3. 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.