Features of Application of Monte Carlo Method with Markov Chain Algorithms in Bayesian Data Analysis-Reference-Cited by-同舟云学术

Features of Application of Monte Carlo Method with Markov Chain Algorithms in Bayesian Data Analysis

Published:2019-11-02 Issue: Volume: Page:361-376
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing IV
language:
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Author:

Bidyuk Peter^ORCID,Matsuki Yoshio^ORCID,Gozhyj Aleksandr^ORCID,Beglytsia Volodymyr^ORCID,Kalinina Irina^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-33695-0_25

Reference24 articles.

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3. Bidyuk, P., Gozhyj, A., Kalinina, I., Gozhyj, V.: Analysis of uncertainty types for model building and forecasting dynamic processes. In: Advances in Intelligent Systems and Computing II, vol. 689, pp. 66–78. Springer, Cham (2017)

4. Blasco, A., Sorensen, D., Bidanel, J.P.: Bayesian inference of genetic parameters and selection response for litter size components in pigs. Genetics 149, 301–306 (1998)

5. Casella, G., George, E.I.: Explaining the Gibbs sampler. Am. Stat. 46, 167–174 (1992)

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