If we let p' be the

prior probability that a Fisher, if present, will be detected in a single survey at a single site, then the likelihood of a single non-detection is 1-p'.

Bratko we realized that my ad-hoc statistical method was almost the same as the Nai've Bayesian classifier (NB), however lacking the

prior probability of the class in the NB formula.

Where in, P[(u,v,w)] is the

prior probability of (u,v,w); the conditional probability P[(u,v,w) s[c.sub.k]] reflects random links between codes and semantic concepts and can be obtained from user feedback or training samples.

Triangular fuzzy Description Notation probability number [5 10 15] Extremely low probability EL [15 20 25] Low probability L [25 30 35] Low to medium LM [35 40 45] Medium to low ML [45 50 55] Medium probability M [55 60 65] Medium to high MH [65 70 75] High to medium HM [75 80 85] High probability H [85 90 95] Extremely high probability EH Table 3:

Prior probability of weather states.

If the

prior probability P([H.sub.0]), [alpha], and [beta] are known, the credibility of Y can be obtained.

More seriously, when the CPS server holds certain side information such as the

prior probability of query contents, cloaking based solutions will suffer from further privacy breach.

A

prior probability is based on the knowledge provided by expert of the process or obtained by learning methode or algorithm from an experimental or experience feedback database [5].

So, if the

prior probability of each focal element can be obtained accurately, the absolutely right probability of the reasonable evidence source can be calculated by the equation

If I then told you that my 10 was red, you'd update your

prior probability to 2/52.

To evaluate the probability of a hypothesis, the Bayesian probabilist specifies some

prior probability, which is then updated to a posterior probability in the light of new, relevant data.

The following algorithm calculates the posterior probabilities for each signal type: for each i signal type we initialize [P.sup.0](F) = {[p.sub.1],..., [p.sub.i],..., [p.sub.n]}, where n is the number of fault cases and pi is the

prior probability associated for fault case [F.sub.i].

At any given point in the trial, a decision maker has an estimation of the [probability] that a certain conclusion, such as the guilt of the defendant, is true (called the

prior probability); the decision maker is provided with additional evidence (reflected in the theorem as a likelihood ratio), which enables a revision of that estimation, increasing or decreasing the estimate of the probability of guilt (called the posterior probability).