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.

The kids also need to be clear about the proof of this particular theorem in the whole process so that there is no doubt at any point in time and they can successfully determine the probability without any kind of hassle. Apart from this being clear about the basic formula existence for the random variables is another very important thing to be considered so that overall goals are very easily achieved and there will be no chance of any kind of problem in the whole process. Having a clear idea about the implementation of this particular formula for the continuous random variable is another very important aspect so that overall goals are very easily achieved and there will be no problem in the whole process at any point in time.

Normally people get confused between the concept of conditional probability and Bayes theorem but it is important to note down that conditional probability is the probability of event A which will be based upon the occurrence of the event B and all the other hand Bayes theorem is based upon utilisation of the definition of conditional probability in which there will be two conditional probability is in the whole process.

Hence, depending upon platforms like **Cuemath** is the best way of having a good command over the chapter of **probability** and becoming successful in the long run.

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