The Bayesian Probability Calculator helps users apply Bayes’ Theorem to update probabilities based on new evidence. It is essential for decision-making in uncertain conditions, widely used in fields like finance, healthcare, machine learning, and risk assessment.

Key Features

✅ Applies Bayes’ Theorem – Computes posterior probability based on prior probability and new evidence.
✅ Supports Multiple Events – Handles single or multiple conditional probability scenarios.
✅ Instant Calculation – Provides real-time probability updates with detailed explanations.
✅ Graphical Representation – Visualizes probability distributions using dynamic charts.
✅ PDF Report Generation – Export detailed results with calculations and probability graphs.

Use Cases

📌 Medical Diagnosis – Estimate the probability of a disease given test results.
📌 Risk Assessment & Decision-Making – Update probabilities in business, finance, and cybersecurity.
📌 Machine Learning & AI – Compute probabilistic predictions in Bayesian networks.
📌 Market Research – Adjust market forecasts based on new consumer data.

How It Works

1️⃣ Input Prior Probability – Enter the initial belief (prior probability) of an event occurring.
2️⃣ Enter Conditional Probabilities – Provide likelihood values (e.g., sensitivity/specificity in medical testing).
3️⃣ Run Calculation – The tool applies Bayes’ Theorem:

$$P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}$$

where P(A|B) is the updated probability, P(B|A) is the likelihood, P(A) is the prior, and P(B) is the total probability of the observed event.
4️⃣ Interpret Results – Review the new probability and graphical insights.
5️⃣ Download PDF Report – Export the results for documentation and decision-making.

This tool simplifies Bayesian probability calculations, helping users make informed decisions based on evolving data. 🚀