{"uid":"cap_mcs64H4Bl8VAhRkcgTWm4","slug":"kihustle-bayes-probability-calculator-c0df7d82","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/api/v1/probability-bayes","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_1pl9UDQpGE7k-K5bZHeMx","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 posterior probability using Bayes' theorem given prior probability and conditional likelihoods","exampleAgentPrompt":"Calculate the Bayesian posterior probability for me: the prior probability of event A is 0.3, the probability of observing B given A is 0.8, and the probability of observing B given not-A is 0.2.","exampleUseCases":[{"title":"Medical test result interpretation","prompt":"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?"},{"title":"Spam filter probability update","prompt":"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?"},{"title":"Investment risk Bayesian update","prompt":"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?"}],"resultDescription":"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)].","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T19:01:50.164Z","isFirstParty":false}