What is Bayesian statistics and how is it used?

What is Bayesian statistics and how is it used?

Bayesian statistics is a statistical paradigm that uses Bayes' theorem to update the probability of a hypothesis as more evidence becomes available. It's widely used in various fields such as medicine, finance, and machine learning.

Can you explain Bayes' theorem in simple terms?

Sure! Bayes' theorem describes how to update the probability of a hypothesis based on new evidence. It combines prior knowledge with new data to give a more accurate probability.

What are some real-world applications of Bayesian statistics?

Bayesian statistics is used in clinical trials to evaluate treatment effects, in spam filtering to classify emails, and in machine learning for predictive modeling.

How does Bayesian statistics differ from frequentist statistics?

The main difference is that Bayesian statistics incorporates prior beliefs and updates them with new data, while frequentist statistics relies solely on the data at hand without incorporating prior information.

Are there any limitations to using Bayesian statistics?

Yes, some limitations include the need for prior distributions, which can be subjective, and computational complexity in some models.