Computational Bayesian statistics: an introduction
Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the ext...
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2019
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| Series: | Institute of Mathematical Statistics textbooks
11 |
| Links: | https://doi.org/10.1017/9781108646185 |
| Summary: | Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics. |
| Physical Description: | 1 Online-Ressource (xi, 243 Seiten) |
| ISBN: | 9781108646185 |
Staff View
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| spelling | Turkman, Maria Antónia Amaral 1949- Computational Bayesian statistics an introduction M. Antónia Amaral Turkman, Carlos Daniel Paulino, Peter Müller Cambridge Cambridge University Press 2019 1 Online-Ressource (xi, 243 Seiten) txt c cr Institute of Mathematical Statistics textbooks 11 Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics. Müller, Peter 1963 August 9- Paulino, Carlos Daniel Erscheint auch als Druck-Ausgabe 9781108481038 Erscheint auch als Druck-Ausgabe 9781108703741 |
| spellingShingle | Turkman, Maria Antónia Amaral 1949- Computational Bayesian statistics an introduction |
| title | Computational Bayesian statistics an introduction |
| title_auth | Computational Bayesian statistics an introduction |
| title_exact_search | Computational Bayesian statistics an introduction |
| title_full | Computational Bayesian statistics an introduction M. Antónia Amaral Turkman, Carlos Daniel Paulino, Peter Müller |
| title_fullStr | Computational Bayesian statistics an introduction M. Antónia Amaral Turkman, Carlos Daniel Paulino, Peter Müller |
| title_full_unstemmed | Computational Bayesian statistics an introduction M. Antónia Amaral Turkman, Carlos Daniel Paulino, Peter Müller |
| title_short | Computational Bayesian statistics |
| title_sort | computational bayesian statistics an introduction |
| title_sub | an introduction |
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