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Time Series: Modeling, Computation, and Inference by Raquel Prado, Mike West

2011; Wiley; Volume: 79; Issue: 2 Linguagem: Inglês

10.1111/j.1751-5823.2011.00149_6.x

1751-5823

Christian P. Robert,

Bayesian Methods and Mixture Models

International Statistical ReviewVolume 79, Issue 2 p. 277-279 Short Book ReviewsEditor: Simo Puntanen Time Series: Modeling, Computation, and Inference by Raquel Prado, Mike West Christian P. Robert, Christian P. Robert Ceremade—Université Paris-Dauphine, Bureau B638Place du Maréchal de Lattre de Tassigny, 75775 Paris Cedex 16, France[email protected]Search for more papers by this author Christian P. Robert, Christian P. Robert Ceremade—Université Paris-Dauphine, Bureau B638Place du Maréchal de Lattre de Tassigny, 75775 Paris Cedex 16, France[email protected]Search for more papers by this author First published: 02 August 2011 https://doi.org/10.1111/j.1751-5823.2011.00149_6.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References Brockwell, P. & Davis, P. (2009). Time Series: Theory and Methods, 2nd ed. New York : Springer. Google Scholar Carvalho, C., Johannes, M., Lopes, H. & Polson, N. (2010). Particle learning and smoothing. Stat. Science, 25, 88–106. 10.1214/10-STS325 Web of Science®Google Scholar Chopin, N., Iacobucci, A., Marin, J.-M., Mengersen, K., Robert, C. P., Ryder, R. & Schäfer, C. (2010). On particle learning. arXiv:1006.0554. Google Scholar Green, P. (1995). Reversible jump MCMC computation and Bayesian model determination. Biometrika, 82, 711–732. 10.1093/biomet/82.4.711 Web of Science®Google Scholar Petris, G., Petrone, S. & Campagnoli, P. (2009). Dynamic Linear Models with R. New York: Springer. 10.1007/b135794_2 Google Scholar Prado, R. (2010). Multi-state models for mental fatigue. The Handbook of Applied Bayesian Analysis, Eds. A. O’Hagan & M. West, pp. 845–874. Oxford : Oxford University Press. Google Scholar Ruiz-Cárdenas, R., Krainski, E. T. & Rue, H. (2010). Fitting dynamic models using integrated nested Laplace approximations—INLA. Technical Report 12, Department of mathematical sciences , Norwegian University of Science and Technology . Google Scholar West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models. New York : Springer. Google Scholar Volume79, Issue2August 2011Pages 277-279 ReferencesRelatedInformation