## Bayesian modeling using WinBUGS

Posted in Books, R, Statistics, University life with tags , , , , , , , , , , , on November 7, 2011 by xi'an

Yes, yet another Bayesian textbook: Ioannis Ntzoufras’ Bayesian modeling using WinBUGS was published in 2009 and it got an honourable mention at the 2009 PROSE Award. (Nice acronym for a book award! All the mathematics books awarded that year were actually statistics books.) Bayesian modeling using WinBUGS is rather similar to the more recent Bayesian ideas and data analysis that I reviewed last week and hence I am afraid the review will draw a comparison between both books. (Which is a bit unfair to Bayesian modeling using WinBUGS since I reviewed Bayesian ideas and data analysis  on its own! However, I will presumably write my CHANCE column as a joint review.)

As history has proved, the main reason why Bayesian theory was unable to establish a foothold as a well accepted quantitative approach for data analysis was the intractability involved in the calculation of the posterior distribution.” Chap. 1, p.1

The book launches into a very quick introduction to Bayesian analysis, since, by page 15, we are “done” with linear regression and conjugate priors. This is somehow softened by the inclusion at the end of the chapter of a few examples, including one on the Greek football  team in Euro 2004, but nothing comparable with Christensen et al.’s initial chapter of motivating examples. Chapter 2 on MCMC methods follows the same pattern:  a quick and dense introduction in about ten pages, followed by 40 pages of illuminating examples, worked out in full detail. CODA is described in an Appendix. Compared with Bayesian ideas and data analysis, Bayesian modeling using WinBUGS spends time introducing WinBUGS and Chapter 3 acts like a 20 page user manual, while Chapter 4 corresponds to the WinBUGS example manual. Chapter 5 gets back to a more statistical aspect, the processing of regression models (including Zellner’s g-prior). up to ANOVA. Chapter 6 extends the previous chapter to categorical variables and the ANCOVA model, as well as the 2006-2007 English premier league. Chapter 7 moves to the standard generalised linear models, with an extension in Chapter 8 to count data, zero inflated models, and survival data. Chapter 9 covers hierarchical models, with mixed models, longitudinal data, and the water polo World Cup 2000. Continue reading