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Distributional data analysis is a branch of nonparametric statistics that is related to functional data analysis.It is concerned with random objects that are probability distributions, i.e., the statistical analysis of samples of random distributions where each atom of a sample is a distribution.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
Distributional cost-effectiveness analysis (DCEA) is an extension of cost-effectiveness analysis (CEA) that incorporates concern for both the average levels of outcomes as well as the distribution of outcomes.
In statistics, especially in Bayesian statistics, the kernel of a probability density function (pdf) or probability mass function (pmf) is the form of the pdf or pmf in which any factors that are not functions of any of the variables in the domain are omitted. [1] Note that such factors may well be functions of the parameters of the
Commercial intelligence (CI) is the process of defining, gathering, analyzing, distributing accurate, and relevant intelligence regarding the products, customers, competitors, business environment, and the organization itself. [1] This methodical program affects the organization's tactics, decisions and operations. [2]
In business intelligence, data classification is "the construction of some kind of a method for making judgments for a continuing sequence of cases, where each new case must be assigned to one of pre-defined classes." [1] Data Classification has close ties to data clustering, but where data clustering is descriptive, data classification is ...
Standard examples of each, all of which are linear classifiers, are: generative classifiers: naive Bayes classifier and; linear discriminant analysis; discriminative model: logistic regression; In application to classification, one wishes to go from an observation x to a label y (or probability distribution on labels
The structured analysis of competing hypotheses offers analysts an improvement over the limitations of the original ACH. [9] The SACH maximizes the possible hypotheses by allowing the analyst to split one hypothesis into two complex ones. For example, two tested hypotheses could be that Iraq has WMD or Iraq does not have WMD.