What is Bayesian statistics and how is it used in machine learning?

What is Bayesian statistics and how is it used in machine learning?

Bayesian statistics is a statistical paradigm that uses Bayes' theorem to update the probability of a hypothesis as more evidence becomes available. In machine learning, it's used for tasks like classification, regression, and model selection.

Can you give me an example of how it's applied in a machine learning model?

Sure! One common application is in spam detection. A Bayesian classifier can be trained on labeled emails to determine the probability that a new email is spam based on its features.

What are the advantages of using Bayesian methods over traditional methods?

Bayesian methods allow for incorporating prior knowledge, provide a natural way to handle uncertainty, and can update predictions as new data comes in.

Are there any limitations to using Bayesian statistics?

Yes, some limitations include computational complexity, especially with large datasets, and the need for careful selection of prior distributions.