Archive for Bayesian ideas and data analysis

Bayes by the Bay

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , , , , , , on August 28, 2012 by xi'an

No, no, this is not an announcement for a meeting on an Australian beach (which is Bayes on the Beach, taking place next November (6-8) on the Sunshine Coast and is organised by Kerrie Mengersen’s BRAG, at QUT, that I just left! With Robert Wolpert as the international keynote speaker and Matt Wand as the Australian keynote speaker.) Bayes by the Bay is “a pedagogical workshop on Bayesian methods in Science” organised by the Institute of Mathematical Sciences, based in the CIT campus in Chennai. It is taking place on January 4-8, 2013, in Pondichéry. (To use the French spelling of this former comptoir of French India…) Just prior to the ISBA Varanasi meeting on Bayesian Statistics.

Great: the webpage for the workshop uses the attached picture of Pierre-Simon (de) Laplace, rather than the unlikely picture of Thomas Bayes found all over the place (incl. this blog!). This was also the case in Christensen et al.’s Bayesian ideas and data analysis. So maybe there is a trend there. I also like the name “Bayes by the Bay“, as it reminds me of a kid song we used to sing to/with our kids when they were young, “down by the bay“, after a summer vacation with Anne and George Casella…

Coincidentally, my re-read of Laplace’s Théorie Analytique des Probabilitiés just appeared (in English) in the Boletim ISBrA, the dynamic Brazilian branch of ISBA.

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