Sentence examples for stochastic grammar from inspiring English sources

The representation used by the new algorithm is based on a stochastic grammar supplemented with two novel features: conditional production weights and the dynamic partitioning of ordinal ranges.

Our work forms the basis for the development of a stochastic grammar which may be used to explore the syntax of the Indus script in greater detail.

That way, the random sampling based on a stochastic grammar - building heavily on the use of small floating point numbers - is translated into an unranking algorithm using integer values only.

Here, we propose a novel framework, called evidential grammars, which extends stochastic grammars by replacing probabilities by belief functions.

Examples include Hidden Markov Models and its variations such as coupled HMMs [31] and Layered HMMs [32], Stochastic Grammars [33], and Conditional Random Fields [34].

As a result, stochastic grammars provide a robust means for inferring unobserved quantities from the data, e.g., the dependence of recruitment dynamics on unmeasured latent variables.

This includes the Pareto-style combination of stochastic grammars with other (non-probabilistic) types of information.

To add in stochastic grammars, one needs to provide a stochastic evaluation algebra, together with support for parameter training.

However, some applications require the inclusion of grammar rules, and in these cases generative models, such as Hidden Markov Models (HMMs) and Stochastic Grammars, are routinely applied.

The following references provide a certainly incomplete list of DP-approaches to RNA pseudoknot structure prediction using various structure classes characterized in terms of recursion equations and/or stochastic grammars: [ 9, 15- 26].

The main contribution of the paper is the application of stochastic regular grammars, with learning capability, to generate the robots team's language.