# What are posterior odds?

**posterior odds**= Bayes factor × prior

**odds**. From this formula, we see that the Bayes' factor (BF) tells us whether the data provides evidence for or against the hypothesis. • If BF > 1 then the

**posterior odds**are greater than the prior

**odds**. So the data provides evidence for the hypothesis.

Subsequently, one may also ask, how are posterior odds calculated?

In light of the matching evidence (E_{m}), the **posterior odds** that the search has landed on the person who deposited the DNA at the crime scene, P(H_{1}|E_{m}) / P(H_{2}|E_{m}), are given by: P(H1|Em)P(H2|Em)=P(Em|H1)P(Em|H2) x P(H1)P(H2) .

Additionally, what is posterior probability example? You can think of **posterior probability** as an adjustment on prior **probability**: **Posterior probability** = prior **probability** + new evidence (called **likelihood**). For **example**, historical data suggests that around 60% of students who start college will graduate within 6 years. This is the prior **probability**.

Also question is, what is a posterior probability in statistics?

A **posterior probability**, in Bayesian **statistics**, is the revised or updated **probability** of an event occurring after taking into consideration new information. In **statistical** terms, the **posterior probability** is the **probability** of event A occurring given that event B has occurred.

What is prior likelihood and posterior?

**Prior**: **Probability distribution** representing knowledge or uncertainty of a data object **prior** or before observing it. **Posterior**: Conditional **probability distribution** representing what parameters are likely after observing the data object. **Likelihood**: The **probability** of falling under a specific category or class.