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KiHustle Bayes Probability Calculator
x402
KiHustle Bayes Probability Calculator is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Computes posterior probability using Bayes' theorem given prior probability, likelihood, and false positive rate
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with a 'result' field containing the computed posterior probability value and a 'status' field confirming success. The posterior represents P(A|B) — the updated probability of A being true given the observed evidence B.
Example use cases
Medical test false positive analysis
“I need to figure out the real probability someone has a disease after testing positive. The base rate in the population is 0.01, the test correctly detects disease 95% of the time, and gives a false positive 5% of the time — can you run the Bayesian calculation for me?”
Spam filter probability update
“Help me update my spam detection probability using Bayes theorem: prior probability a message is spam is 0.3, probability of seeing this word pattern in spam is 0.8, and probability of seeing it in legitimate mail is 0.1.”
Risk assessment belief update
“I want to use Bayes theorem to update my risk estimate — the initial probability of a project failing is 0.25, the probability we'd see these warning signs given failure is 0.7, and the probability of seeing them even if the project succeeds is 0.15. What's the updated probability?”
Technical details
Endpoint
POSThttps://kihustle.tech/agents/api/v1/probability-bayesWhen to prefer
Use this endpoint when you need a quick, pay-per-call Bayesian posterior probability calculation without setting up your own math library or statistical service. It is well-suited for AI agents that need to update belief probabilities on-the-fly given prior and conditional probabilities, particularly in lightweight automation pipelines where installing a full stats library is impractical.
Side effects
- Missing required fields (p_a, p_b_given_a, or p_b_given_not_a) may return an error or unexpected result
- Probability values outside the 0–1 range may cause computation errors or invalid results
- Division by zero if p_b_given_a and p_b_given_not_a are both 0 (denominator collapses)
- Vague or generic response schema means the exact error format is not well-documented
- Service may be experimental with limited reliability guarantees
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| p_a | number | |
| p_b_given_a | number | |
| p_b_given_not_a | number |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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