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
- Prior should be your best estimate BEFORE seeing any new evidence (0-1)
- Evidence values should be independent of each other when possible
- Weights should reflect your trust in each evidence source (sum normalized internally)
- Call repeatedly as new evidence arrives — the posterior becomes the next prior
- 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.
