Электронная библиотека БГУ: 2019. Computer Data Analysis and Modeling : Stochastics and Data Science

Даты публикации Авторы Заглавия Темы

2019. Computer Data Analysis and Modeling : Stochastics and Data Science : [74] Главная страница коллекции Статистика

This collection of papers includes proceedings of the Twelfth International Conference “Computer Data Analysis and Modeling: Stochastics and Data Science” organized by the Belarusian State University and held in September 2019 in Minsk. Papers are reviewed by qualified researchers from Austria, Belarus, Lithuania, Russia, Ukraine.

The papers are devoted to the topical problems: robust and nonparametric data analysis; statistical analysis of time series and forecasting; multivariate data analysis; design of experiments; probability and statistical analysis of discrete data; econometric analysis and modeling; survey analysis and official statistics; computer intensive methods, algorithms and software; computer data analysis in applications.

For specialists who work in the fields of mathematical statistics and its applications, computer data analysis, data science and statistical software development.

Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the Twelfth Intern. Conf., Minsk, Sept. 18-22, 2019. – Minsk : BSU, 2019. – 355 p.

Editors:

Prof. Dr. P. Filzmoser, Prof. Dr. Yu. Kharin

ISBN 978-985-566-811-5

При полном или частичном использовании материалов ссылка на сайт Электронной библиотеки БГУ (www.elib.bsu.by) обязательна.

Ресурсы коллекции (Сортировка по Дата поступления в По убыванию порядке): 41 по 60 из 74

< предыдущий следующий >

Дата выпуска	Заглавие	Автор(ы)
2019	Probabilistic data analysis for predicting mean time before critical integrity losses of complex system when explicit quantitative requirements to integrity are not specified	Kostogryzov, A.; Nistratov, A.; Nistratov, G.
2019	About the processes in the changing fractional dimension space	Kornet, M. E.; Medvedev, A. V.; Mihov, E. D.
2019	New method to minimize the stress in multidimensional scaling	Dzemyda, G.; Sabaliauskas, M.
2019	Influence of the training set on the prediction stability in estimation of acute pancreatitis severity	Mangalova, E.; Chubarova, O.; Melekh, D.
2019	Detecting changes in the dependence structure of a time series	Dürre, A.; Fried, R.
2019	Statistical analysis of count conditionally nonlinear autoregressive time series by frequencies-based estimators	Kharin, Yu. S.; Kislach, M. I.
2019	Bayesian binomial regression model with a latent Gaussian field for analysis of epigenetic data	Hubin, A.; Storvik, G.; Grini, P.; Butenko, M.
2019	On some new aspects in acquisition and analysis of brain electrical activity	Kolchin, A. V.; Ionkina, H. G.
2019	Simulation of adaptive control system with conflict flows of non-homogeneous requests	Fedotkin, M. A.; Kudryavtsev, E. V.
2019	On statistical estimation of transition probabilities matrix for Markov chain under incomplete observations	Kharin, Yu. S.; Dernakova, O. V.
2019	Performance and robustness in sequential testing of hypotheses	Kharin, A. Yu.; Ton That Tu
2019	Expected error rate in linear discrimination of balanced spatial Gaussian time series	Karaliute, M.; Ducinskas, K.; Saltyte-Vaisiauske, L.
2019	Probabilistic approach in factorization by elliptic curve method	Dermenzhy, I.; Vostrov, G.
2019	On parameter estimation of double-censored stationary Gaussian time series	Badziahin, I. A.
2019	Predicting the success of antegrade chronic total occlusion recanalization using machine learning	Abramovich, M. S.; Kozlovsky, V. V.; Stelmashok, V. I.; Polonetsky, O. L.
2019	Tourism in Belarus: indicators, satellite account and surveys	Bokun, N.
2019	Approximate formulas for expectation of functionals from solution to linear Skorohod stochastic differential equation	Egorov, A. D.; Zherelo, A. V.
2019	Modeling baltic market indices: a comparison of models	Belovas, I.
2019	Robust analogs of the Qn-estimate of scale for the Student distributions	Shevlyakov, G. L.; Shirokov, I. S.; Litvinova, V. G.
2019	Estimation of conditional survival function under dependent random censored data	Abdushukurov, A. A.

Ресурсы коллекции (Сортировка по Дата поступления в По убыванию порядке): 41 по 60 из 74

< предыдущий следующий >

2019. Computer Data Analysis and Modeling : Stochastics and Data Science : [74] Главная страница коллекции Статистика

Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the Twelfth Intern. Conf., Minsk, Sept. 18-22, 2019. – Minsk : BSU, 2019. – 355 p.

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