# prior probability

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## prior probability

n
(Statistics) statistics the probability assigned to a parameter or to an event in advance of any empirical evidence, often subjectively or on the assumption of the principle of indifference. Compare posterior probability
Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014
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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).

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