Revised Probabilities and Posterior Probabilities

How do you explain revised probability?

In Bayesian statistics, what is the posterior probability and how is it derived?

Explanation:

In Bayesian statistics, the posterior probability is the updated or revised probability of an event occurring after taking into account new information. Using Bayes' theorem, the posterior probability is derived by updating the prior probability.

When we have additional information, we can revise our initial probabilities. The posterior probability is a way to incorporate this new information into our calculations to obtain an updated probability.

Bayes' theorem provides a mathematical formula for updating the prior probability based on new evidence to calculate the posterior probability. This helps us make more informed decisions and predictions.

Understanding and using posterior probabilities is essential in Bayesian statistics to make accurate and relevant interpretations of data.