Bayes' Theorem is used to revise probabilities. The new (revised) ﻿probabilities are called
a. ﻿prior probabilities.
b. ﻿sample probabilities.
c. ﻿survey probabilities.
d. ﻿posterior probabilities.

The new (revised) probabilities are called:

**d. posterior probabilities.**

Bayes' Theorem updates the probability of a hypothesis based on new evidence, and the resulting updated probability is known as the posterior probability.

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Here are some related questions:
1. What is Bayes' Theorem and how is it derived?
2. What are prior probabilities in the context of Bayes' Theorem?
3. How do you calculate posterior probabilities using Bayes' Theorem?
4. Can you provide an example of Bayes' Theorem in action?
5. What is the difference between prior and posterior probabilities?
6. How is Bayes' Theorem applied in real-world scenarios?
7. What are some common misconceptions about Bayes' Theorem?
8. How does Bayes' Theorem relate to conditional probability?

**Tip:** Bayes' Theorem is especially powerful in scenarios where new data or evidence needs to be considered to update the likelihood of a hypothesis.

Discover how Bayes' Theorem revises probabilities with detailed insights. Explore the concept of posterior probabilities and their application in probability theory.

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