What is Bayes Theorem?
Imagine you flip a coin. Intuitively, you know the probability of landing on heads or tails is 50%. This is where basic probability comes in. Bayes theorem, however, goes a step further. It allows you to update this probability (called the prior probability) after you’ve observed new evidence (called the likelihood). In simpler terms, it lets you revise your initial guess about something (the coin toss) based on new information (seeing the result).
Mathematically, Bayes theorem is expressed as:
[Tex]P(A | B) = (P(B | A) * P(A)) / P(B)[/Tex]
Here,
- P(A | B) is the posterior probability, the likelihood of event A happening given that event B has already occurred.
- P(B | A) is the likelihood, the probability of observing B if A is true.
- P(A) is the prior probability, our initial belief about the chance of event A occurring.
- P(B) is the total probability of event B happening (irrespective of A).
Applications of Bayes theorem in Artificial Intelligence
The world of artificial intelligence thrives on data and the ability to make predictions based on that data. But what happens when there’s uncertainty involved? This is where Bayes’ theorem steps in, offering a powerful tool to navigate probabilistic situations and refine artificial models. In this tutorial we will discuss applications of Bayes Theorem in Artificial Intelligence.