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In computer science, Backus–Naur form (BNF; / ˌ b æ k ə s ˈ n aʊər /; Backus normal form) is a notation used to describe the syntax of programming languages or other formal languages. It was developed by John Backus and Peter Naur. BNF can be described as a metasyntax notation for context-free grammars. Backus–Naur form is applied ...
Greek letters (e.g. θ, β) are commonly used to denote unknown parameters (population parameters). [3]A tilde (~) denotes "has the probability distribution of". Placing a hat, or caret (also known as a circumflex), over a true parameter denotes an estimator of it, e.g., ^ is an estimator for .
BNF may refer to: Science. Backus–Naur form, a formal grammar notation in computer science; Biological nitrogen fixation; ... Statistics; Cookie statement ...
The representation of a grammar is a set of syntax diagrams. Each diagram defines a "nonterminal" stage in a process. There is a main diagram which defines the language in the following way: to belong to the language, a word must describe a path in the main diagram.
BNF (Backus normal form, or Backus–Naur form) and EBNF (extended Backus-Naur form) are the two main notation techniques for context-free grammars.; Drakon-charts are a graphical notation of algorithms and procedural knowledge.
Some of the widely used formal metalanguages for computer languages are Backus–Naur form (BNF), extended Backus–Naur form (EBNF), Wirth syntax notation (WSN), and augmented Backus–Naur form (ABNF). Metalanguages have their own metasyntax each composed of terminal symbols, nonterminal symbols, and metasymbols. A terminal symbol, such as a ...
Wirth syntax notation (WSN) is a metasyntax, that is, a formal way to describe formal languages. Originally proposed by Niklaus Wirth in 1977 as an alternative to Backus–Naur form (BNF). It has several advantages over BNF in that it contains an explicit iteration construct, and it avoids the use of an explicit symbol for the empty string ...
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...