Assuming that the

prior probability P(S[C.sub.k]) follows a uniform distribution, Formula (7) can be simplified as:

The

prior probability P([H.sub.0]) of every year was shown in Table 1.

Given the

prior probability of query contents together with an integer k which specifies the privacy level, MLA generates a set of reports, each of which consists of k distinct query contents, together with probability distribution on the report set for each query content.

If the

prior probability of each focal element can not be obtained accurately and any focal element has no advantage in the prior knowledge, denoted by P([a.sub.1]) = P([a.sub.2]) = L = P([a.sub.n]), the absolutely right probability of the reasonable evidence source can be calculated as follows:

For the remaining M-N feature which did not exceed the threshold of signal type i, we update the

prior probability by the following equation:

(57.) When the

prior probability is already near 1, neither strong nor weak evidence will make much of a difference.

Bayesian learning starts with some initial information about an event X which enables the researcher to estimate the probability of event X occurring; in turn, in the next period, if additional or better information becomes available a new probability is estimated (the posterior probability) given the probability estimated in the previous period (the

prior probability) and so forth for any n periods.

Here, [[pi].sub.0] is the

prior probability from the perspective of the DM that the defendant is guilty, and 1 - [[pi].sub.0] is the corresponding

prior probability that the defendant is not guilty.

where P([X.sub.i], [X.sub.2], ..., [X.sub.n]) is the joint probability, P([X.sub.i]) is the

prior probability, [mathematical expression not reproducible] is the conditional probability, and [mathematical expression not reproducible] is the posterior probability.

One specifies the

prior probability for the modes, and the other defines that there are elements, representing the monitor outputs, in the observation vector which are equally distributed under different modes.

Second, mimicking the influence of correct prior information we set the

prior probability of the true variables to 0.8 and the others to 0.1.

For the inference engine, we need

prior probability knowledge.