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  <channel rdf:about="https://elib.bsu.by:443/handle/123456789/18851">
    <title>ЭБ Раздел:</title>
    <link>https://elib.bsu.by:443/handle/123456789/18851</link>
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        <rdf:li rdf:resource="https://elib.bsu.by:443/handle/123456789/344859" />
        <rdf:li rdf:resource="https://elib.bsu.by:443/handle/123456789/343517" />
        <rdf:li rdf:resource="https://elib.bsu.by:443/handle/123456789/342054" />
        <rdf:li rdf:resource="https://elib.bsu.by:443/handle/123456789/340009" />
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    <dc:date>2026-04-21T08:46:42Z</dc:date>
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  <item rdf:about="https://elib.bsu.by:443/handle/123456789/344859">
    <title>Detection of embeddings in binary Markov chains</title>
    <link>https://elib.bsu.by:443/handle/123456789/344859</link>
    <description>Заглавие документа: Detection of embeddings in binary Markov chains
Авторы: Kharin, Y.S.; Vecherko, E.V.
Аннотация: The paper is concerned with problems in steganography on the detection of embeddings and statistical estimation of positions at which message bits are embedded. Binary stationary Markov chains with known or unknown matrices of transition probabilities are used as mathematical models of cover sequences (container hles). Based on the runs statistics and the likelihood ratio statistic, statistical tests are constructed for detecting the presence of embeddings. For a family of contiguous alternatives, the asymptotic power of statistical tests based on the runs statistics is found. An algorithm of polynomial complexity is developed for the statistical estimation of positions with embedded bits. Results of computer experiments are presen</description>
    <dc:date>2016-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://elib.bsu.by:443/handle/123456789/343517">
    <title>Нейросетевые модели биномиальных временных рядов в задачах анализа данных</title>
    <link>https://elib.bsu.by:443/handle/123456789/343517</link>
    <description>Заглавие документа: Нейросетевые модели биномиальных временных рядов в задачах анализа данных
Авторы: Харин, Ю.С.
Аннотация: В данном сообщении рассматриваются задачи построения нейросетевых моделей дискретных временных рядов и использования их для компьютерного анализа данных. Представлено новое семейство нейросетевых моделей дискретных временных рядов, позволяющих аппроксимировать любой тип стохастической зависимости состояний временного ряда от его предыстории. Установлены условия эргодичности и отношение эквивалентности для этих моделей. Построены состоятельные статистические оценки параметров моделей и алгоритмы компьютерного анализа данных с использованием нейросетевых моделей: алгоритмы оценивания параметров, прогнозирования и распознавания образов.</description>
    <dc:date>2021-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://elib.bsu.by:443/handle/123456789/342054">
    <title>Statistical Analysis of Poisson Conditionally Nonlinear Autoregressive Time Series by Frequencies-Based Estimators</title>
    <link>https://elib.bsu.by:443/handle/123456789/342054</link>
    <description>Заглавие документа: Statistical Analysis of Poisson Conditionally Nonlinear Autoregressive Time Series by Frequencies-Based Estimators
Авторы: Kharin, Yu.; Kislach, M.
Аннотация: Poisson conditionally nonlinear autoregressive model is proposed for integer-valued time series. Frequencies-based estimators (FBE) for model parameters, statistical forecasting statistics, and statistical tests for this model are constructed; their performance is analyzed theoretically and by computer experiments on simulated and real data.</description>
    <dc:date>2020-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://elib.bsu.by:443/handle/123456789/340009">
    <title>A statistical study of the spatial and temporal variability of temperature and wind fields</title>
    <link>https://elib.bsu.by:443/handle/123456789/340009</link>
    <description>Заглавие документа: A statistical study of the spatial and temporal variability of temperature and wind fields
Авторы: Beliauskene, E.; Ustinova, I.; Konstantinov, L.
Аннотация: The study focuses on the analysis of long-term meteorological observation data obtained from a network of stations in Central Russia, aimed at identifying spatiotemporal patterns in the distribution of temperature and wind. The results may serve as a basis for improving methods of forecasting meteorological fields, as well as, for modeling and verification of atmospheric processes</description>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
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