Aitchison, J. (1986), The Statistical Analysis of Compositional Data, Monographs on statistics and applied probability, Chapman and Hall, London.

   Aitchison, J. and Egozcue, J. (2005), ‘Compositional data analysis: Where are we and where should we be heading?’, Mathematical Geology 37(7), 829–850.

   Allen, J., Watts, W. and Huntley, B. (2000), ‘Weichselian palynostratigraphy, palaeovegetation and palaeoenvironment: the record from lago grande di monticchio, southern italy’, Quaternary International 73(74), 91–110.

   Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2003), Hierarchical Modeling and Analysis for Spatial Data, CRC, Boca Raton.

   Bartlein, P., Prentice, I. and Webb III, T. (1986), ‘Climatic response surfaces from pollen data for some eastern north american taxa’, Journal of Biogeography 13, 35–57.

   Bellman, R. (1957), Dynamic Programming, Princeton University Press, Princeton, NJ.

   Bernardo, J. and Smith, A. F. M. (1994), Bayesian Theory, John Wiley, New York.

   Bhattacharya, S. (2004), Importance Resampling MCMC: a methodology for cross-validation in inverse problems and its applications in model assessment, PhD thesis, University of Dublin, Trinity College, Dept. of Statistics, Trinity College Dublin, Dublin 2, Ireland.

   Bhattacharya, S. and Haslett, J. (2008), ‘Importance re-sampling mcmc for cross-validation in inverse problems’, Bayesian Analysis 2(2), 385–408.

   Birks, H. and Birks, H. H. (1980), Quaternary Palaeoecology, University Park Press, Baltimore, USA.

   Connor, R. and Mosimann, J. (1969), ‘Concepts of independence for proportions with a generalization of the dirichlet distribution’, Journal of the American Statistical Association 64, 194–206.

   Finkenstdt, B., Held, L. and Isham, V. (2006), Statistical Methods for Spatio-Temporal Systems, Monographs on statistics and applied probability, Chapman and Hall, London.

   Gelfand, A. E. (1996), Model determination using sampling-based methods, in W. Gilks, S. Richardson and D. Spiegelhalter, eds, ‘Markov Chain Monte Carlo in Practice’, Interdisciplinary Statistics, Chapman and Hall, London, pp. 145–162.

   Gelfand, A. E., Dey, D. K. and Chang, H. (1992), Model determination using predictive distributions with implementation via sampling methods(with discussion), in J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith, eds, ‘Bayesian Statistics 4’, Oxford University Press, pp. 147–167.

   Gelfand, A. E. and Smith, A. F. M. (1990), ‘Sampling-based approaches to calculating marginal densities’, Journal of the American Statistical Society 85, 398–409.

   Gilks, W. R., Richardson, S. and Spiegelhalter, D. J., eds (1996), Markov chain Monte Carlo in practice, Chapman & Hall, London.

   Haslett, J., Bhattacharya, S., Michael, M. W., Salter-Townshend, Wilson, S. P., Allen, J., Huntley, B. and Mitchell, F. (2006), ‘Bayesian palaeoclimate reconstruction’, Journal of the Royal Statisticcal Society: Series A 169(3), 1–36.

   Hastings, W. K. (1970), ‘Monte carlo sampling methods using markov chains and their applications’, Biometrika 57, 97–109.

   Heilbron, D. C. (1994), ‘Zero-altered and other regression models for count data with added zeros’, Biometrical Journal 36, 531–547.

   Holden, P. B., Mackay, A. W. and Simpson, G. L. (2008), ‘A bayesian palaeoenvironmental transfer function model for acidified lakes’, Journal of Paleolimnology 39(4), 551–566.

   Huntley, B. (1993), ‘The use of climate response surfaces to reconstruct palaeoclimate from quaternary pollen and plant macrofossil data’, Philosophical Transactions of the Royal Society of London Series B - Biological Sciences 341, 215–223.

   Kneib, T. (2006), Mixed model based inference in structured additive regression, PhD thesis, LMU München, Faculty of Mathematics, Computer Science and Statistics, LMU Mnchen.

   Korhola, A., Vasko, K., Toivonen, H. T. and Olander, H. (2002), ‘Holocene temperature changes in northern fennoscandia reconstructed from chironomids using bayesian modelling’, Quaternary Science Reviews 21(16–17), 1841–1860.

   Krutchkoff, R. (1967), ‘Classical and inverse regression methods of calibration’, Technometrics 9, 425–439.

   Lambert, D. (1992), ‘Zero-inflated poisson regression, with an application to defects in manufacturing’, Technometrics 34, 1–14.

   Martin, T. G., Wintle, B. A., Rhodes, J. R., Kuhnert, P. M., Field, S. A., Low-Choy, S. J., Tyre, A. J. and Possingham, H. P. (2005), ‘Zero tolerance ecology: Improving ecological inference by modelling the source of zero observations’, Ecology Letters 8, 1235–1246.

   Metropolis, N., Rosenbluth, A., Rosenbluth, R., Teller, A. and Teller, E. (1953), ‘Equation of state calculations by fast computing machines’, Journal of Chemical Physics 21(6), 1087–1092.

   Mullahy, J. (1986), ‘Specification and testing of some modified count data models’, Journal of Econometrics 33, 341–365.

   Oakley, J. and O’Hagan, A. (2002), ‘Bayesian inference for the uncertainty distribution of computer model outputs’, Biometrika 89, 769–784.

   Prentice, I. C., Bartlein, P. J. and Webb, T. I. (1991), ‘Vegetation and climate change in eastern north america since the last glacial maximum’, Ecology 72, 2038–2056.

   Ridout, M., Demetrio, C. G. B. and Hinde, J. (1998), ‘Models for count data with many zeros’, Proceedings of the XIXth International Biometric Conference Invited Papers, 179–192.

   Rougier, J. (2008), ‘Comment on article by Sansó et al’, Bayesian Analysis 3(1), 45–56.

   Rue, H. and Held, L. (2005), Gaussian Markov Random Fields: Theory and Applications, Vol. 104 of Monographs on Statistics and Applied Probability, Chapman & Hall, London.

   Rue, H., Martino, S. and Chopin, N. (2008), ‘Approximate bayesian inference for latent gaussian models by using integrated nested laplace approximations’, Journal of the Royal Statistical Society: Series B 71(4), 1–35.

   Salter-Townshend, M. and Haslett, J. (2006), ‘Zero-inflation of compositional data’, Proceedings of the 21st International Workshop on Statistical Modelling pp. 448–456.

   Smol, J. P., Last, W. M. and Birks, H. J. B. (2001), Tracking Environmental Change Using Lake Sediments: Terrestrial, Algal, and Siliceous Indicators, Springer.

   ter Braak, C. J. F. (1995), ‘Non-linear methods for multivariate statistical calibration and their use in paleoecology: a comparison of inverse and classical approaches’, Chemometrics and Intelligent Laboratory Systems 28, 165–180.

   Tian, G.-L., Wang, K. and Geng, Z. (2003), ‘Bayesian computation for contingency tables with incomplete cell counts’, Statistica Sinica 13, 189–206.

   Toivonen, H. T. T., Mannila, H., Korhola, A. and Olander, H. (2001), ‘Applying bayesian statistics to organism-based environmental reconstruction’, Ecological Applications 11(2), 618–630.

   Vasko, K., Toivonen, H. T. and Korhola, A. (2000), ‘A bayesian multinomial gaussian response model for organism-based environmental reconstruction’, Journal of Paleolimnology 24, 243–250.

   Vehtari, A. and Lampinen, J. (2002), ‘Bayesian model assessment and comparison using cross-validation predictive densities’, Neural Computation 14(10), 2439–2468.

   Wong, T.-T. (1998), ‘Generalized dirichlet distribution in bayesian analysis’, Applied Mathematics and Computation 97(2–3), 165–181.