Today the methods of applied statistics have penetrated very
different fields of knowledge, including the investigation oftexts
ofvarious origins. These "texts" may be considered as signal
sequences of different kinds, long genetic codes, graphic
representations (which may be coded and represented by a "text"),
as well as actual narrative texts (for example, historical
chronicles, originals, documents, etc. ). One ofthe most important
problems arising here is to recognize dependent text, i. e. , texts
which have a measure of "resemblance", arising from some kind of
"common origin". For instance, in pattern-recognition problems, it
is essential to identify from a large set of "patterns" a pattern
that is "closest" to a given one; in studying long signal
sequences, it is important to recognize "homogeneous subsequences"
and the places of their junction. This includes, in particular, the
well-known change-point prob lern, which is given considerable
attention in mathematical statistics and the theory of stochastic
processes. As applied to the study of narrative texts, the problern
of recognizing depen dent and independent texts ( e . g. ,
chronicles) Ieads to the problern offinding texts having a common
source, i. e. , the sameoriginal (such texts are naturally called
dependent), or, on the contrary, having different sources (such
texts are natu rally called independent). Clearly, such problems
are exceedingly complicated, and therefore the appearance of new
empirico-statistical recognition methods which, along with the
classical approaches, may prove useful in concrete studies (e. g. ,
source determination) is welcome.
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