About Conditional Probability

Introduction

     Suppose the two events are not independent, that is the occurrence of one depends on the occurrence of other, then how do we compute This can be explained by conditional probability. Joint probability is the probability of two events in conjunction. That is, it is the probability of both events together. The joint probability of A and B is written or
     Baye's theorem is named after the British mathematician Thomas Bayes who published it in a research paper in 1763. It gives one of the important applications of the conditional probabilities by using the additional information supplied by the experiment or the past records. The updated conditional probability of A, given I and the outcome of the event B, is known as the posterior probability, P(A|B,I).

Conditional Probability

     Let A and B be any two events associated with a random experiment. The probability of occurrence of event A when the event B has already occurred is called the conditional probability of A when B is given and is denoted as P(A/B).