Incorporating Bayes’ Theorem in Machine Learning

Bayes’ theorem is a fundamental concept in probability and statistics that can be applied to machine learning. It provides a way to update the likelihood of a hypothesis as more evidence or data becomes available. In machine learning, Bayes’ theorem is used to estimate the probability of a hypothesis given observed data, known as the posterior probability.

Bayes’ theorem is incorporated in machine learning for a variety of reasons, which are as follows:

• Incorporating prior knowledge: It allows us to incorporate prior knowledge or beliefs about the problem into our predictions. This can be especially useful in situations where we have limited data.

• Handling missing data: We can use it to estimate the probability of a hypothesis even when some of the data is missing.

• Robustness to noise: It is robust to noise in the data, meaning it can ...