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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 and conditional likelihoods
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 P(A|B) and a 'status' field indicating success. The posterior is calculated as P(A)*P(B|A) / [P(A)*P(B|A) + P(not A)*P(B|not A)].
Example use cases
Medical test result interpretation
“I need to figure out the probability that a patient actually has a disease given a positive test. The base rate of the disease is 0.01, the test correctly detects it 95% of the time (P(positive|disease) = 0.95), and gives a false positive 10% of the time (P(positive|no disease) = 0.1). Can you run a Bayes calculation?”
Spam filter probability update
“I'm building a spam filter and want to know the posterior probability that an email is spam. The prior probability of spam is 0.4, the probability of seeing the word 'free' in a spam email is 0.9, and the probability of seeing it in a legit email is 0.1. Can you compute the Bayesian posterior?”
Investment risk Bayesian update
“Help me update my probability estimate that a startup will succeed. My prior probability is 0.25, the probability that a funded startup shows this revenue traction given success is 0.7, and the probability it shows this traction even if it fails is 0.15. What's the posterior probability using Bayes?”
Technical details
Endpoint
POSThttps://kihustle.tech/api/v1/probability-bayesWhen to prefer
Choose this endpoint when you need a simple, fast, single-call Bayesian posterior computation from three probability inputs. It is ideal for agents that need to update beliefs based on evidence without building a full probabilistic model. Best for one-shot Bayes calculations rather than complex multi-variable inference networks.
Side effects
- Missing required input fields (p_a, p_b_given_a, or p_b_given_not_a) may cause a processing error
- Probability values outside [0,1] range may produce invalid results or errors
- Division by zero if both P(B|A)*P(A) and P(B|not A)*P(not A) sum to zero
- Vague or non-numeric inputs will fail schema validation
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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