Bayesian Inference
A statistical method that updates the probability of an outcome as new evidence becomes available using Bayes' theorem.
What is Bayesian Inference?
Bayesian inference begins with an initial belief about the probability of an event, known as the prior probability. When new evidence is observed, this belief is updated to produce a posterior probability. This approach allows models to continuously incorporate new information and quantify uncertainty when making predictions or decisions.
Why is Bayesian Inference Important?
Real-world decisions are often made with incomplete or uncertain information. Bayesian inference provides a systematic way to combine existing knowledge with new evidence. In machine learning, it helps models estimate uncertainty, update predictions as data changes, and make more informed decisions when available information is limited.
Common use cases
Bayesian inference is commonly used in medical diagnosis, risk assessment, forecasting, fraud detection, recommendation systems, scientific research, and probabilistic machine learning.