What do you mean by Bayes' theorem ?

Hi, Relates the probability of the occurrence of an event to the occurrence or non-occurrence of an associated event. For example, the probability of drawing an ace from a pack of cards is 0.077 (4 ÷ 52). If two cards are drawn at random, the probability of the second card being an ace depends on whether the first card is an ace or not: if it is, then the probability of the second card being an ace is 0.058 (3 ÷ 52); if not, the probability remains 0.077. Bayes' theorem provides a mathematical rule for revising an estimate or forecast in light of experience and observation. read less

Bayes' theorem is stated mathematically as the P(A|B) = {P(B | A)\, P(A)}{P(B)}, where A and B are events. P(A) and P(B) are the probabilities of A and B independent of each other. P(A|B), a conditional probability, is the probability of A given that B is true. P(B|A), is the probability of B given that A is true read less

Bayes' Theorem is a theorem of probability theory originally stated by the Thomas Bayes. It can be seen as a way of understanding how the probability that a theory is true is affected by a new piece of evidence. It has been used in a wide variety of contexts, ranging from marine biology to the development of "Bayesian" spam blockers for email systems. It obeys the likelihood principle. If two distinct sampling designs yield proportional likelihood functions for , then all inferences about should be identical from these two designs. Classical inference does not in general obey the likelihood principle. It provides interpretable answers, such as “the true parameter has a probability of 0.95 of falling in a 95% credible interval.” read less

Bayes' theorem is stated mathematically as the following equation:[6] P(A|B) = \frac{P(B | A)\, P(A)}{P(B)}, where A and B are events. P(A) and P(B) are the probabilities of A and B independent of each other. P(A|B), a conditional probability, is the probability of A given that B is true. P(B|A), is the probability of B given that A is true. read less

Baye's relates current probability to prior probability.Basically used in conditional probability. Bayes theorem is mathematically stated as P(A/B)=[P(B/A)*P(A)/P(B)] * P(A)& P(B) a are the probabilities of A and B independent of each other *P(A|B)= conditional probability,that is the probability of A given that B is true. P(B|A)= the probability of B given that A is true. for example: read less