<a target="_blank" href="https://www.almabetter.com/bytes/tutorials/artificial-intelligence/bayes-theorem-in-ai">Bayes' Theorem</a> is a fundamental concept in probability theory and artificial intelligence. It describes the probability of an event based on prior knowledge of conditions related to the event. Mathematically, it is represented as:
[ P(A \mid B) = \frac{P(B \mid A) \times P(A)}{P(B)} ]
Where:
<ul>
<li>( P(A \mid B) ) is the probability of event ( A ) given event ( B ) has occurred.
</li>
<li>( P(B \mid A) ) is the probability of event ( B ) given event ( A ) has occurred.
</li>
<li>( P(A) ) and ( P(B) ) are the probabilities of events ( A ) and ( B ) respectively.
</li>
</ul>
Bayes' Theorem is widely used in AI for tasks like probabilistic reasoning, spam filtering, medical diagnosis, and machine learning algorithms such as Naive Bayes classifiers.

[Bayes' Theorem](https://www.almabetter.com/bytes/tutorials/artificial-intelligence/bayes-theorem-in-ai) is a fundamental concept in probability theory and artificial intelligence. It describes the probability of an event based on prior knowledge of conditions related to the event. Mathematically, it is represented as:

\[ P(A \\mid B) = \\frac{P(B \\mid A) \\times P(A)}{P(B)} \]

Where:

* ( P(A \\mid B) ) is the probability of event ( A ) given event ( B ) has occurred.
    
* ( P(B \\mid A) ) is the probability of event ( B ) given event ( A ) has occurred.
    
* ( P(A) ) and ( P(B) ) are the probabilities of events ( A ) and ( B ) respectively.
    

Bayes' Theorem is widely used in AI for tasks like probabilistic reasoning, spam filtering, medical diagnosis, and machine learning algorithms such as Naive Bayes classifiers.

Bayes' Theorem in AI (Artificial Intelligence)