{"uid":"cap_SnINqE7AVHULhMGBhbqgn","slug":"kihustle-bayes-probability-calculator-b6d6b846","name":"KiHustle Bayes Probability Calculator","description":"Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.","url":"https://kihustle.tech/bot-bazaar/api/v1/probability-bayes?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"p_a":{"type":"number"},"p_b_given_a":{"type":"number"},"p_b_given_not_a":{"type":"number"}}},"responseSchema":{"type":"json","example":{"result":"processed","status":"success"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_YbwABJNjCc_0kD9BetN3D","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes Bayesian posterior probability P(A|B) given prior probability, likelihood, and marginal likelihood inputs","exampleAgentPrompt":"Calculate the Bayesian posterior probability for me: my prior P(A) is 0.2, the probability of observing B given A is 0.9, and the probability of observing B given not-A is 0.15.","exampleUseCases":[{"title":"Medical test result interpretation","prompt":"I have a disease screening test result. The base rate of the disease is 0.01, the test correctly detects the disease 95% of the time, and gives a false positive 5% of the time. What's the actual probability I have the disease given a positive test?"},{"title":"Spam email classification","prompt":"Help me figure out the Bayesian probability that an email is spam. My prior for spam is 0.3, the probability of seeing this keyword pattern in spam is 0.85, and the probability of seeing it in non-spam is 0.05."},{"title":"Investment signal reliability","prompt":"I want to update my belief about a trade signal being correct. My prior confidence is 0.4, the signal fires correctly 70% of the time when the outcome is good, and fires incorrectly 20% of the time when the outcome is bad. What's the posterior probability?"}],"resultDescription":"Returns a JSON object with a computed result field containing the Bayesian posterior probability P(A|B), along with a success status indicator.","failureModes":["Missing required fields (p_a, p_b_given_a, or p_b_given_not_a) result in a processing error","Invalid probability values outside [0,1] range may cause unexpected results","Division by zero if P(B) computes to zero (when P(B|A)*P(A) + P(B|not-A)*P(not-A) = 0)","Non-numeric input types may cause a failure response","Generic error response with status field only, limited error detail"],"whenToPreferThis":"Use this endpoint when you need a quick, cheap ($0.002) server-side Bayes theorem computation without implementing the formula yourself. Ideal when an AI agent needs to programmatically update probabilities based on evidence in an automated pipeline. Prefer this over local computation when you want a standardized, auditable calculation step in an agent workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T18:59:15.171Z","isFirstParty":false,"canonicalSlug":"kihustle-bayes-probability-calculator-c0df7d82"}