| BBN | Bayesian Belief Network |
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| Bayesian hypothesis | An array of surmised values of a parameter to be severally explored in the light of a current set of data, with logical symmetry being preserved among all. The merits of each hypothesis entertained are based on quantity, the prior probability. The probability of the data conditional on the hypothesis is computed as the conditional probability for each; the product of the two for each hypothesis is the joint probability, and the ratio of each joint probability to the sum of all the joint probabilities is the posterior probability for that hypothesis. Unlike the Neyman-Pearson test of hypotheses, the answer is a statement about the hypothesis, not about the sample conditional on the hypothesis. No hypothesis is preferred or prevails by default. The procedure may be applied recursively any number of times, as the data becomes available. (05 Mar 2000) |
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| bayesian |
of or relating to statistical methods based on Bayes' theorem
출처: wordnet.princeton.edu/perl/webwn
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| bayesian statistics |
An area of statistics that uses Bayes' theorem to update prior belief about a probability or population parameter to posterior belief. (page 785)
출처: highered.mcgraw-hill.com/sites/0072470267/student_...
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| bayesian s. |
a somewhat controversial statistical methodology that, unlike conventional statistics, which treats population parameters as fixed (though unknown) values, treats parameters as random variables with a specified probability distribution, termed the prior (or a priori) distribution. Bayes' theorem is then used to convert the probability distribution of an observable statistic (treated as a conditional probability for a given parameter value) to a conditional probability distribution of the parameter values for a given value of the observable statistic. This distribution is termed the posterior (or a posteriori) distribution because it assigns a probability to each parameter value that depends on the observed data. The controversial point is the prior distribution, which represents a subjective opinion of the experimenter as to the a priori credibility of the various parameter values; for example, in estimating the probability of the presence of a particular disease given a positive test result, the prior distribution represents the experimenter's judgment of the prevalence of the disease in the population under study.
출처: www.mercksource.com/pp/us/cns/cns_health_library.j...
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| bayesian statistics |
Statistics which incorporate prior knowledge and accumulated experience into probability calculations.
출처: www.dmdsurveys.com/dmd_site3/terminology_pages/ter...
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제품명 |
판매사 |
보험코드 | 성분/함량 | 구분/보험급여 |
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제품명 |
판매사 |
보험코드 | 성분/함량 | 구분/보험급여 |
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