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In numerical linear algebra, the alternating-direction implicit (ADI) method is an iterative method used to solve Sylvester matrix equations. It is a popular method for solving the large matrix equations that arise in systems theory and control , [ 1 ] and can be formulated to construct solutions in a memory-efficient, factored form.
In 1982 the U.S. Department of Justice Merger Guidelines introduced the SSNIP test as a new method for defining markets and for measuring market power directly. In the EU it was used for the first time in the Nestlé/Perrier case in 1992 and has been officially recognized by the European Commission in its "Commission's Notice for the Definition of the Relevant Market" in 1997.
Unconventional computing (also known as alternative computing or nonstandard computation) is computing by any of a wide range of new or unusual methods. The term unconventional computation was coined by Cristian S. Calude and John Casti and used at the First International Conference on Unconventional Models of Computation [ 1 ] in 1998.
Quasi-likelihood estimation is one way of allowing for overdispersion, that is, greater variability in the data than would be expected from the statistical model used. It is most often used with models for count data or grouped binary data, i.e. data that would otherwise be modelled using the Poisson or binomial distribution.
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Although the concept choice models is widely understood and practiced these days, it is often difficult to acquire hands-on knowledge in simulating choice models.While many stat packages provide useful tools to simulate, researchers attempting to test and simulate new choice models with data often encounter problems from as simple as scaling parameter to misspecification.
Heuer outlines the ACH process in considerable depth in his book, Psychology of Intelligence Analysis. [1] It consists of the following steps: Hypothesis – The first step of the process is to identify all potential hypotheses, preferably using a group of analysts with different perspectives to brainstorm the possibilities.
Network intelligence (NI) is a technology that builds on the concepts and capabilities of deep packet inspection (DPI), packet capture and business intelligence (BI). It examines, in real time, IP data packets that cross communications networks by identifying the protocols used and extracting packet content and metadata for rapid analysis of data relationships and communications patterns.