baye's theorem
 Probability is an expectation or assumption about occurring of event. It tells about the measure whether an event will occur or not. Suppose, a coin is tossed then the assumption or expectation of the occurrence of head or tail is termed as probability and the probability of the occurrence of head and tail will be equal that is two.
Bayes theorem can be defined as a theorem which has different interpretations. It concerns two events and relates inverse representations of probabilities concerning these events. Suppose, the occurrence of probability of a and the probability of b are given and the occurrence of b when a has already occurred is also given, now we need to determine the probability of a when b has already occurred is represented by P(a/b). Then here the bayes theorem is used and it will be determined as P(a/b) will be equals to P(b/a) multiplied with the probability of a that is represented as P(a) and divided with the probability of b that is represented as P(b). Thus the whole formula can be represented as P(a/b) = [P(b/a) P(a)]/ P(b). If in any problem, more than two variables are given then it will also be used in the bayes theorem.
In general terms, P(a/b) can be equated as P(a á´§ b) / P(b) where P(b) must not be equals to zero in order to avoid the infinite occurrence of error. Similarly, P(b/a) will be equals to P(a á´§ b) / P(a) if P(a) is not equals to zero. Bayes rule for three or more occurrence of events can be given as P(a| b á´§ c) will be equals to probability of the intersection of all three events that is represented as P(a á´§ b á´§ c) divided by P(b á´§ c). It can also be further equated as [P(c|a á´§ b) P(a á´§ b)] divided by P(c|b) P(b). For understanding the topic make a bar graph.
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