oraclaw-bayesian

# 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` ```json { "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.