Baye's Rule in Artificial Intelligence Methods

[iam-jerry]

Brief Summary:

Baye's Rule or theorem is a formula that describes how to update the probabilities of hypotheses when given evidence. Many modern machine learning techniques rely on Baye's rule, e.g., spam filters use Bayesian updating to determine whether an email is real or spam, given the words in the email. It can also be used to weigh conflicting pieces of evidence in medicine, in court of law, and in many scientific disciplines. The Baye's rule can be expressed in many forms. The simplest is in terms of odds. The purpose of the formula is to update the Prior Odds when new information becomes available, to obtaining the Posterior Odds or the odds after obtaining the information.

Key Takeaways:

To understand: 1) the prior and posterior odds of available new information 2) the likelihood ratio probability of an event happening (or not happening) 3) Baye's rule in practice: breast cancer screening

References:

https://brilliant.org/wiki/bayes-theorem/ https://course.elementsofai.com/3/2

[ninjaginja]

great topic @iam-jerry - approved

[ninjaginja]

Closing due to inactivity. You may request to reopen this issue once you are ready to submit a final draft for review.

[iam-jerry]

Hi @ninjaginja! I'm ready to submit the final draft of my article for review. Could you please reopen the issue? Thank you.