How does Bayesian inference work in machine learning?

Can you explain how Bayesian inference is used in machine learning?

Sure! Bayesian inference is a statistical method that updates the probability for a hypothesis as more evidence or information becomes available.

What are some practical applications of Bayesian inference in machine learning?

It's used in various applications like spam detection, recommendation systems, and even in medical diagnosis to update beliefs based on new data.

How does it differ from traditional statistical methods?

Unlike traditional methods that often rely on fixed parameters, Bayesian inference incorporates prior knowledge and updates beliefs with new evidence.

Can you give an example of a Bayesian model?

A common example is the Naive Bayes classifier, which uses Bayes' theorem to predict the category of data points based on prior probabilities.