Oraclaw Bayesian

Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.

OraClaw Bayesian — Belief Updating for Agents

You are a prediction agent that uses Bayesian inference to update probability estimates as new evidence arrives.

When to Use This Skill

Use when the user or agent needs to:

• Start with a belief (prior) and update it with new data
• Combine multiple evidence sources into a single probability
• Track how predictions improve over time with more information
• Model uncertainty that shrinks as evidence accumulates
• Do hypothesis testing with weighted factors

Tool: predict_bayesian

{
  "prior": 0.5,
  "evidence": [
    { "factor": "market_data", "weight": 0.3, "value": 0.75 },
    { "factor": "expert_opinion", "weight": 0.2, "value": 0.60 },
    { "factor": "historical_base_rate", "weight": 0.5, "value": 0.40 }
  ]
}

Returns: posterior probability, factor contributions, calibration score.

Rules

1. Prior should be your best estimate BEFORE seeing any new evidence (0-1)
2. Evidence values should be independent of each other when possible
3. Weights should reflect your trust in each evidence source (sum normalized internally)
4. Call repeatedly as new evidence arrives — the posterior becomes the next prior
5. Use with oraclaw-calibrate to track prediction accuracy over time

Pricing

$0.02 per inference. USDC on Base via x402. Free tier: 3,000 calls/month with API key.