For more information about the book, you can read its Preface and two sample chapters, chapter 4 and chapter 15.

- Foreword by Jim Nichols
- Preface
- Acknowledgements

- Introduction
- Introduction to the Bayesian analysis of a statistical model
*WinBUGS**A*first session in*WinBUGS:*The "model of the mean"- Running
*WinBUGS*from*R*via*R2WinBUGS* - Key components of (generalised) linear models: Statistical distributions and the linear predictor
- t-Test: Equal and unequal variances
- Normal linear regression
- Normal one-way ANOVA
- Normal two-way ANOVA
- General linear model (ANCOVA)
- Linear mixed-effects model
- Introduction to the Generalised linear model (GLM): Poisson t-test
- Overdispersion and offsets in the GLM
- Poisson ANCOVA
- Poisson mixed-effects model (Poisson GLMM)
- Binomial t-test
- Binomial ANCOVA
- Binomial mixed-effects model (Binomial GLMM)
- Non-standard GLMMs 1: Site-occupancy species distribution model
- Non-standard GLMMs 2: Binomial mixture model for the modeling of abundance
- Conclusions

- References

Some people have suggested to me that I could have used 'seeds' in the random-number generators, e.g., the R function rnorm(). This would have guaranteed that everybody executing the simulation code always ends up with the same data set, i.e., with the same realization of the random process that we imagine has generated our data. Similarly, up to the chance element inherent in Markov chain Monte Carlo, everybody would have gotten more similar results in the BUGS analysis as well. This might be comforting to some readers, because you could be even more sure that your solution is indeed the 'correct' one, when it matches up the one in the book. I could have achieved the same goal by providing on this website my data sets that I used for producing the results in the book. Indeed, I have saved them and originally had intended to make them available to you.

However, I finally decided against the use of seeds as well as against making available my own data sets. My reason for this is that people greatly overestimate the importance of a specific data set at hand.

In real life, the data set at hand, however hard-won it may be (e.g., the
result of four years of data collection during my PhD), is nothing more than a
single realization of the stochastic process about which I want to learn
something. Most of the time, the data set has no particular importance, except
for being our link to that random process, which is what we are really
interested in. It is important to think more clearly about the relationship
between the data set at hand and that random process, and about the relative
importance of the two. By *not* singling out one particular realization from
that process, I emphasize the (usually) secondary role of the particular data
set and the primary role of the underlying stochastic process.

Text file with all *R/WinBUGS* code: R_WB_code.txt

I am also grateful for any comments you might have on the book.

Erratum: Errata_and_tips.html

The current book is targeted at ecologists, but the statistical models are simply regression models with their ordinary extensions: the generalized linear model and random-effects models. There are only two chapters on the kinds of models that ecologists use for more specialized inference about populations (the last two chapters). My colleague Michael Schaub and I have just written a more advanced sequel to this book, which is scheduled to be available from Academic Press in December 2011. We call it the BPA book. It covers a fairly comprehensive selection of specialized ecological statistical models for the analysis of populations using WinBUGS, not unlike the landmark book by Royle and Dorazio (2008), but in a similar format as the current book. For more information about our new book, see its full table of contents: BPA_TOC.pdf |

This page last revised: 4 Feb 2011