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the avl boost documentation set uses inclusive language to provide a comfortable experience for users who are navigating documents. inclusive language is language that is free of bias and discrimination. read more about how cisco is using inclusive language.


the avl boost documentation set uses hidden markov models (hmms) to illustrate the most frequently used concepts and features in the documentation. hidden markov models are a method of representing sequential data as a probability distribution over possible sequences. hmms are used extensively in text analysis and have been shown to be applicable to modeling sequences of user actions. because the avl boost documentation is using hmms to illustrate concepts and features, there is a small possibility that a given concept or feature is not represented by the intended hmm. hmms are used because they are a powerful and flexible tool, and they are less error-prone than other types of models. use of hmms also saves time by avoiding the need to manually model or modify sequences of user actions. hmms can be used with any kind of sequential data and are particularly useful for user actions because they are automatically customizable. hidden markov models are used extensively in text analysis and have been shown to be applicable to modeling sequences of user actions. because the avl boost documentation is using hidden markov models to illustrate concepts and features, there is a small possibility that a given concept or feature is not represented by the intended hmm. hidden markov models are used because they are a powerful and flexible tool, and they are less error-prone than other types of models. 84d34552a1