What is Bayes Theorem explain with examples?

What is Bayes Theorem explain with examples?

Bayes theorem is also known as the formula for the probability of “causes”. For example: if we have to calculate the probability of taking a blue ball from the second bag out of three different bags of balls, where each bag contains three different colour balls viz. red, blue, black.

How Bayes theorem can be implemented in real life and give an example?

For example, if a disease is related to age, then, using Bayes’ theorem, a person’s age can be used to more accurately assess the probability that they have the disease, compared to the assessment of the probability of disease made without knowledge of the person’s age.

How do you use Bayes theorem examples?

Bayes’ Theorem Example #1 A could mean the event “Patient has liver disease.” Past data tells you that 10% of patients entering your clinic have liver disease. P(A) = 0.10. B could mean the litmus test that “Patient is an alcoholic.” Five percent of the clinic’s patients are alcoholics. P(B) = 0.05.

How do you solve Bayes Theorem?

The formula is:

  1. P(A|B) = P(A) P(B|A)P(B)
  2. P(Man|Pink) = P(Man) P(Pink|Man)P(Pink)
  3. P(Man|Pink) = 0.4 × 0.1250.25 = 0.2.
  4. Both ways get the same result of ss+t+u+v.
  5. P(A|B) = P(A) P(B|A)P(B)
  6. P(Allergy|Yes) = P(Allergy) P(Yes|Allergy)P(Yes)
  7. P(Allergy|Yes) = 1% × 80.7% = 7.48%

Where is Bayes theorem used?

In finance, Bayes’ Theorem can be used to rate the risk of lending money to potential borrowers. The theorem is also called Bayes’ Rule or Bayes’ Law and is the foundation of the field of Bayesian statistics.

Which of the following are applications of Bayes Theorem?

We will discuss the three main applications of Bayes’ Theorem: Naive Bayes’ Classifiers. Discriminant Functions and Decision Surfaces. Bayesian Parameter Estimation.

How Bayes theorem is used in artificial intelligence?

Bayes’ theorem allows updating the probability prediction of an event by observing new information of the real world. Example: If cancer corresponds to one’s age then by using Bayes’ theorem, we can determine the probability of cancer more accurately with the help of age.

When should we use Bayes theorem?

The Bayes theorem describes the probability of an event based on the prior knowledge of the conditions that might be related to the event. If we know the conditional probability , we can use the bayes rule to find out the reverse probabilities .

How is Bayes theorem used in machine learning?

Bayes Theorem is also used widely in machine learning, where it is a simple, effective way to predict classes with precision and accuracy. The Bayesian method of calculating conditional probabilities is used in machine learning applications that involve classification tasks.