Оптимальное соответсвие

wikipedia, “Optimal matching ”, public translation into Russian from English More about this translation.

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Optimal matching

Оптимальное соответсвие

History of edits (Latest: asja_m 1 year, 6 months ago) §

Optimal matching

Оптимальное соответсвие

History of edits (Latest: asja_m 1 year, 6 months ago) §

From Wikipedia, the free encyclopedia

Из Википедии, бесплатной энциклопедии

History of edits (Latest: ViktoriaRejina 1 year, 6 months ago) §

Optimal matching is a sequence analysis method used in social science, to assess the dissimilarity of ordered arrays of tokens that usually represent a time-ordered sequence of socio-economic states two individuals have experienced. Once such distances have been calculated for a set of observations (e.g. individuals in a cohort) classical tools (such as cluster analysis) can be used. The method was tailored to social sciences[1] from a technique originally introduced to study molecular biology (protein or genetic) sequences (see sequence alignment). Optimal matching uses the Needleman-Wunsch algorithm.

Оптимальное соответствие - это метод секвенционального анализа, используемого в социальных науках, чтобы оценить различия внутри упорядоченных массивов данных, которые обычно представлены упорядоченными по времени последовательностями социально-экономических состояний, которые могут принимать индивиды. Когда такие различия будут выявлены внутри множества наблюдений (например, группы индивидов), могут быть применены классические методы исследования (такие как кластерный анализ). Метод был приспособлен для использования в социальных науках из техники, изначально применяемой в исследование молекулярных последовательностей (белков или генов) в биологии (см. линейная последовательность). Метод опимального соответствия использует алгоритм Нидлмана-Вунша.

History of edits (Latest: asja_m 1 year, 6 months ago) §

— различия внутри??? asja_m

которые могут принимать индивиды asja_m

two individuals asja_m

Contents [hide]

Содержание

History of edits (Latest: asja_m 1 year, 6 months ago) §

1 Algorithm

Алгоритм

History of edits (Latest: asja_m 1 year, 6 months ago) §

2 Criticism

Критика

History of edits (Latest: asja_m 1 year, 6 months ago) §

3 Optimal matching in causal modelling

Метод оптимального соответствия в моделировании случайных событий

History of edits (Latest: asja_m 1 year, 6 months ago) §

4 Software

Программное обеспечение

History of edits (Latest: asja_m 1 year, 6 months ago) §

5 References and notes

Ссылки и примечания

History of edits (Latest: asja_m 1 year, 6 months ago) §

Algorithm[edit]

Алгоритм

History of edits (Latest: asja_m 1 year, 6 months ago) §

Let S = (s_1, s_2, s_3, \ldots s_T) be a sequence of states s_i belonging to a finite set of possible states. Let us denote {\mathbf S} the sequence space, i.e. the set of all possible sequences of states.

Optimal matching algorithms work by defining simple operator algebras that manipulate sequences, i.e. a set of operators a_i: {\mathbf S} \rightarrow {\mathbf S}. In the most simple approach, a set composed of only three basic operations to transform sequences is used:

one state s is inserted in the sequence a^{\rm Ins}_{s'} (s_1, s_2, s_3, \ldots s_T) = (s_1, s_2, s_3, \ldots, s', \ldots s_T)

one state is deleted from the sequence a^{\rm Del}_{s_2} (s_1, s_2, s_3, \ldots s_T) = (s_1, s_3, \ldots s_T) and

a state s_1 is replaced (substituted) by state s'_1, a^{\rm Sub}_{s_1,s'_1} (s_1, s_2, s_3, \ldots s_T) = (s'_1, s_2, s_3, \ldots s_T).

Imagine now that a cost c(a_i) \in {\mathbf R}^+_0 is associated to each operator. Given two sequences S_1 and S_2, the idea is to measure the cost of obtaining S_2 from S_1 using operators from the algebra. Let A={a_1, a_2, \ldots a_n} be a sequence of operators such that the application of all the operators of this sequence A to the first sequence S_1 gives the second sequence S_2: S_2 = a_1 \circ a_2 \circ \ldots \circ a_{n} (S_1) where a_1 \circ a_2 denotes the compound operator. To this set we associate the cost c(A) = \sum_{i=1}^n c(a_i), that represents the total cost of the transformation. One should consider at this point that there might exist different such sequences A that transform S_1 into S_2; a reasonable choice is to select the cheapest of such sequences. We thus call distance

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