also be used for sequences of words or almost any type of data. For example, they have been used for extracting features for clustering large sets of satellite earth images and for determining what part of the Earth a particular image came from. They have also been very successful as the first pass in genetic sequence search and in the identification of the species from which short sequences of DNA originated.<ref>{{cite journal | last1 = Tomović | first1 = Andrija | last2 = Janičić | first2 = Predrag | last3 = Kešelj | first3 = Vlado | year = 2006 | title = n-Gram-based classification and unsupervised hierarchical clustering of genome sequences | journal = Computer Methods and Programs in Biomedicine | volume = 81 | issue = 2| pages = 137–153 | doi = 10.1016/j.cmpb.2005.11.007 | pmid = 16423423 }}</ref> n-gram models are often criticized because they lack any explicit representation of long range dependency. This is because the only explicit dependency range is (n − 1) tokens for an n-gram model, and since natural languages incorporate many cases of unbounded dependencies (such as wh-movement), this means that an n-gram model cannot in principle distinguish unbounded dependencies from noise (since long range correlations drop exponentially with distance for any Markov model). For this reason, n-gram models have not made much impact on linguistic theory, where part of the explicit goal is to model such dependencies. Another criticism that has been made is that Markov models of language, including n-gram models, do not explicitly capture the performance/competence distinction. This is because n-gram models are not designed to model linguistic knowledge as such, and make no claims to being (even potentially) complete models of linguistic knowledge; instead, they are used in practical applications. In practice, n-gram models have been shown to be extremely effective in modeling language data, which is a core component in modern statistical language applications. Most modern applications that rely on n-gram based models, such as machine translation applications, do not rely exclusively on such models; instead, they typically also incorporate Bayesian inference. Modern statistical models are typically made up of two parts, a prior distribution describing the inherent likelihood of a possible result and a likelihood function used to assess the compatibility of a possible result with observed data. When a language model is used, it is used as part of the prior distribution (e.g. to gauge