A necessarily subjective and incomplete list of textbooks and papers that are interesting in the context of learning Bayesian statistics, compiled with input from Joe Chipperfield and Jörn Pagel for our summer schools on Bayesian statistics.

## Textbooks

**Basic introductions**

Kéry, M. (2010) Introduction to WinBUGS for Ecologists. Academic Press.

Kruschke, J. F. (2010) Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Academic Press.

McCarthy, M. A. (2007) Bayesian methods for ecology. Cambridge University Press.

**Comprehensive**

Lunn D. et al. (2012) The BUGS Book: A Practical Introduction to Bayesian Analysis. Chapman and Hall/CRC.

Gelman, A.; Carlin, J. B.; Stern, H. S. & Rubin, D. B. (2003) Bayesian Data Analysis. Chapman & Hall, London.

**Hierarchical**

Kéry, M. and Schaub, M. (2011) Bayesian population analysis using WinBUGS. Academic Press.

Banerjee, S. et al. (2009) Hierarchical Modeling and Anallysis for Spatial Data. Chapman and Hall/CRC.

Clark, J. S. and Gelfand, A. E. (2006) Hierarchical Modelling for the Environmental Sciences. Oxford University Press.

## Articles

**Foundations of Bayesian statistics, Bayes vs. Frequentists**

Efron, B. (2013) A 250-year argument: Belief, behavior, and the bootstrap Bulletin Of The American Mathematical Society, 50, 129-146

Gelman, A. & Robert, C. P. (2010) ”Not only defended but also applied”: The perceived absurdity of Bayesian inference ArXiv e-prints

Fisher, R. A. (1922) On the mathematical foundations of theoretical statistics Philos. T. Roy. Soc. A., 222, 309-368

Kass, R. (2011) Statistical inference: The big picture Stat. Sci., 26, 1-9

Jaynes, E. (1976) Confidence intervals vs. Bayesian intervals Foundations of probability theory, statistical inference, and statistical theories of science, 2, 175-257.

**Bayes in Ecology**

Hobbs, N. T. & Hilborn, R. (2006) Alternatives to statistical hypothesis testing in ecology: A guide to self teaching Ecol. Appl., 16, 5-19

Ellison, A. M. (2004) Bayesian inference in ecology Ecol. Lett., 7, 509-520

**Prior choice**

**MCMC sampling**

Andrieu, C.; de Freitas, N.; Doucet, A. & Jordan, M. I. (2003) An introduction to MCMC for machine learning Mach. Learning, 50, 5-43

Andrieu, C. & Thoms, J. (2008) A tutorial on adaptive MCMC Stat. Comput., 18, 343-373.

**Bayesian Model Selection**

Kass, R. E. & Raftery, A. E. (1995) Bayes Factors J. Am. Stat. Assoc., 90, 773-795

**Hierarchical Models**

Wikle, C. K. (2003) Hierarchical Bayesian models for predicting the spread of ecological processes Ecology, 84, 1382-1394

Clark, J. S. (2003) Uncertainty and variability in demography and population growth: A hierarchical approach Ecology, 84, 1370-1381

Clark, J. S. & Gelfand, A. E. (2006) A future for models and data in environmental science. Trends in Ecology & Evolution, 21, 375-380

Cressie, N.; Calder, C. A.; Clark, J. S.; Hoef, J. M. V. & Wikle, C. K. (2009) Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling Ecol. Appl., 19, 553-570

Marion, G.; McInerny, G. J.; Pagel, J.; Catterall, S.; Cook, A. R.; Hartig, F. & O’Hara, R. B. (2012) Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche J. Biogeogr., 39, 2225–2239

Cook, A.; Marion, G.; Butler, A. & Gibson, G. (2007) Bayesian Inference for the Spatio-Temporal Invasion of Alien Species Bull. Math. Biol., 69, 2005-2025

Pagel, J. & Schurr, F. M. (2011) Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics Global Ecol. Biogeogr.

**Approximate Bayesian**

Beaumont, M. A. (2010) Approximate Bayesian computation in evolution and ecology Annu. Rev. Ecol. Evol. Syst., 41, 379-406

Csilléry, K.; Blum, M. G. B.; Gaggiotti, O. E. & François, O. (2010) Approximate Bayesian Computation (ABC) in practice Trends in Ecology & Evolution, 25, 410-418

Hartig, F.; Calabrese, J. M.; Reineking, B.; Wiegand, T. & Huth, A. (2011) Statistical inference for stochastic simulation models – theory and application Ecol. Lett., 14, 816-827

Jabot, F. & Chave, J. (2009) Inferring the parameters of the neutral theory of biodiversity using phylogenetic information and implications for tropical forests Ecol. Lett., 12, 239-248

Comprehensive list at http://approximatebayesiancomputational.wordpress.com/

**Fitting (stochastic) process-based models**

Van Oijen, M.; Rougier, J. & Smith, R. (2005) Bayesian calibration of process-based forest models: bridging the gap between models and data Tree Physiol., 25, 915-927

Hartig, F.; Dyke, J.; Hickler, T.; Higgins, S. I.; O’Hara, R. B.; Scheiter, S. & Huth, A. (2012) Connecting dynamic vegetation models to data – an inverse perspective J. Biogeogr., 39, 2240-2252.