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Venn diagram of (true part in red) In logic and mathematics, the logical biconditional, also known as material biconditional or equivalence or bidirectional implication or biimplication or bientailment, is the logical connective used to conjoin two statements and to form the statement "if and only if" (often abbreviated as "iff " [1]), where is known as the antecedent, and the consequent.
Data Analysis Expressions (DAX) is the native formula and query language for Microsoft PowerPivot, Power BI Desktop and SQL Server Analysis Services (SSAS) Tabular models. DAX includes some of the functions that are used in Excel formulas with additional functions that are designed to work with relational data and perform dynamic aggregation.
The first release of Power BI was based on the Microsoft Excel-based add-ins: Power Query, Power Pivot and Power View. With time, Microsoft also added many additional features like question and answers, enterprise-level data connectivity, and security options via Power BI Gateways. [10] Power BI was first released to the general public on 24 ...
Bidirectional model transformations are an important special case in which a model is input to such a program. Some bidirectional languages are bijective. The bijectivity of a language is a severe restriction of its power, [1] because a bijective language is merely relating two different ways to present the very same information.
In computer science, a bidirectional map is an associative data structure in which the (,) pairs form a one-to-one correspondence. Thus the binary relation is functional in each direction: each v a l u e {\displaystyle value} can also be mapped to a unique k e y {\displaystyle key} .
Andrew Goldberg and others explained the correct termination conditions for the bidirectional version of Dijkstra’s Algorithm. [1] As in A* search, bi-directional search can be guided by a heuristic estimate of the remaining distance to the goal (in the forward tree) or from the start (in the backward tree).
Granger defined the causality relationship based on two principles: [8] [10] The cause happens prior to its effect. The cause has unique information about the future values of its effect.
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. With this form of generative deep learning , the output layer can get information from past (backwards) and future (forward) states simultaneously.