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News
October 14, 2026
At HSE University–St Petersburg, Experts Discuss How AI Changes Tourist Routes and Cultural Heritage
Neural networks are no longer simple travel assistants; now they decide what to include on the route and what to skip. Artificial intelligence changes how people perceive a place and shapes the image of tourist centres. At the II International Scientific and Practical Conference ’Information Technologies in the Preservation and Promotion of Cultural Heritage,’ researchers discussed who controls algorithmic solutions and in what ways, and whether authentic traditions get lost in them.
October 12, 2026
Road Injuries Leading Cause of Death Among Men Aged 10 to 39
Worldwide, road injuries are the leading cause of death among males aged 10–39. An international team of researchers reached this conclusion as part of the Global Burden of Disease Study 2023, analysing data from 204 countries between 1990 and 2023. Their findings have been published in The Lancet Public Health, with HSE University Professor Vasily Vlassov listed among the collaborators.
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.

 

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Variance reduction for Markov chains with application to MCMC

Statistics and Computing. 2020. No. 30. P. 973–997.
Belomestny D., Moulines E., Iosipoi L., Naumov A., Samsonov S.

In this paper we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic variance of these functionals over suitable classes of control variates. A distinctive feature of the proposed approach is its ability to significantly reduce the overall finite sample variance. This feature is theoretically demonstrated by means of a deep non asymptotic analysis of a variance reduced functional as well as by a thorough simulation study. In particular we apply our method to various MCMC Bayesian estimation problems where it favourably compares to the existing variance reduction approaches.

Research target: Computer Science Mathematics
Priority areas: IT and mathematics
Language: English
Full text
DOI
Text on another site
Keywords: MCMCvariance reduction
Publication based on the results of:
Uncertainty quantification in high-dimensional models (2020)
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