| Bayesian Inferencing techniques are named after the Reverend Thomas Bayes, an 18th Century vicar who had an interest in studying statistics. |
His theorem rests on the belief that everything, no matter how unlikely, has a prior probability of being true, ie of occurring.
This probability may be so low that it is, in fact, zero; or, it may be so high that it is, in fact, 1.
But somewhere between and including these two values will be a probability of any event occurring, even if we know nothing more about the situation. This is called the prior probability of occurrence of event H and is denoted as P(H).
But what we want to know is the posterior probability of H - that is to say, the probability of H being true after some evidence has been observed.
If we denote this evidence as E, then this posterior probability can be denoted as P(H|E), which is the conditional probability of H being true given that evidence E has been observed.
We can also denote the probability of evidence E being observed given that hypothesis H is true as P(E|H); and the unconditional probability of evidence E being obeserved as P(E).
Bayes theorem then states that:
This is the basis of the inferencing techniques that XMaster uses today.
(c) Copyright Chris Naylor 2017