2006
DOI: 10.1017/s0269889706001074
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An Example of Statistical Investigation of the Text Eugene Onegin Concerning the Connection of Samples in Chains

Abstract: This study investigates a text excerpt containing 20,000 Russian letters of the alphabet, excluding $\Cprime$ and $\Cdprime$, from Pushkin's novel Eugene Onegin–the entire first chapter and sixteen stanzas of the second.

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Cited by 117 publications
(101 citation statements)
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“…In the event of a missing visit, we classified subjects as though there was no change in status until the subsequent visit. In our study of the natural history of asthma, we focused on quiescent, recrudescent, and persistent asthma states and the betweenexamination transitions between states, using a Markov chain model (11). A Markov chain is a stochastic system that transits from one state to another in The medication-related component of the definition in the last column is limited by our equating the "need" for the medication with "report of taking" the medication under a prescription.…”
Section: Predictor and Outcome Variablesmentioning
confidence: 99%
“…In the event of a missing visit, we classified subjects as though there was no change in status until the subsequent visit. In our study of the natural history of asthma, we focused on quiescent, recrudescent, and persistent asthma states and the betweenexamination transitions between states, using a Markov chain model (11). A Markov chain is a stochastic system that transits from one state to another in The medication-related component of the definition in the last column is limited by our equating the "need" for the medication with "report of taking" the medication under a prescription.…”
Section: Predictor and Outcome Variablesmentioning
confidence: 99%
“…At the core of the new clustering algorithm is the OCP-observation alluded to above, already empirically established in (Markov, 1913(Markov, , 2006, that there is a systematic bias toward alternating adjacent segments along some dimension. To reveal this alternation, one can assume that there is a natural grouping of all segments into two initial sets, called 0 and 1, in such a way that the total number of 0-1 or 1-0 alternations between adjacent segments in a corpus is maximized.…”
Section: General Ocp-based Algorithmmentioning
confidence: 99%
“…N-gram models consider only part of the story taking into account only the previous n words by modelling language as a Markov process of a given order (Markov, 1913). For example, bigram models consider the previous word approximating the whole distribution as:…”
Section: Statistical Language Modelingmentioning
confidence: 99%