 # How to have a clear idea about the concept of the Bayes theorem?

Bayes theorem is the theorem in the world of probability and statistics which has been perfectly named after the name of concerned person Thomas Bayes. This particular theorem is very much successful in terms of determining the probability of the event that will be based upon some other event that has been already occurred. Bayes theorem has a full case of applications because of the interference into the healthcare sector and several other kinds of related things which will ultimately allow people to determine the chances of developing the health problems with the increasing age and several other kinds of related factors. So, having a comprehensive understanding of the concept of the Bayes theorem is important for people because of the practical relevance associated with it.

In very simple terms the concept of the Bayes theorem can be very much successful in terms of determining the conditional probability of the given event A when the event B has already occurred. Bayes theorem is also known as the law of probability and it is considered to be the best method of determining the probability of the event based on the occurrences of the previous events. It can be perfectly used in terms of calculating conditional probability and will always help in calculating the probability-based upon the hypothesis. This particular theorem very well states that the conditional probability of event A will be given depending upon the occurrence of event B and will be equal to the likelihood of the event B given when the probability of an event is there. So the formula over here will be the probability of A/B = probability of B/A into the probability of A divided by the probability of B.

• The probability of A over here means how the event is happening prior knowledge and this will be the hypothesis that will be true before any kind of evidence is present
• The probability of B over here will be the marginalization which will be the probability of observing the evidence
• Probability A/B over here will be how likely A is to happen given that B has already happened and this will be the hypothesis True if given the evidence.
• Probability B over A is how likely B will happen given that A has already happened and this will be the probability of seeing the evidence if the hypothesis is true or not.