Applied Bayesian Modelling (2nd Edition) (Wiley Series in by Peter D. Congdon

By Peter D. Congdon

This ebook offers an obtainable method of Bayesian computing and information research, with an emphasis at the interpretation of actual information units. Following within the culture of the profitable first version, this e-book goals to make a variety of statistical modeling purposes obtainable utilizing demonstrated code that may be effectively tailored to the reader's personal functions.

The second edition has been completely transformed and up to date to take account of advances within the box. a brand new set of labored examples is incorporated. the unconventional element of the 1st version was once the assurance of statistical modeling utilizing WinBUGS and OPENBUGS. this option keeps within the new version in addition to examples utilizing R to expand attraction and for completeness of assurance.

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Applied Bayesian Modelling (2nd Edition) (Wiley Series in Probability and Statistics)

This publication offers an available method of Bayesian computing and information research, with an emphasis at the interpretation of actual info units. Following within the culture of the profitable first version, this publication goals to make a variety of statistical modeling purposes obtainable utilizing demonstrated code that may be conveniently tailored to the reader's personal functions.

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Extra resources for Applied Bayesian Modelling (2nd Edition) (Wiley Series in Probability and Statistics)

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And Gelfand, A. (eds) (2006) Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications. Oxford University Press, Oxford, UK. Collett, D. (1991) Modelling Binary Data. Chapman & Hall, London. Cowles, M. and Carlin, B. (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association, 91(434), 883–904. , Wakefield, J. and Walker, S. (1999) Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables.

In S. Brooks, A. Gelman, G. -L. Meng (eds), Handbook of Markov Chain Monte Carlo. CRC, Boca Raton, FL. , Kadane, J. and O’Hagan, A. (2005) Statistical methods for eliciting probability distributions. Journal of the American Statistical Association, 100(470), 680–701. Gelfand, A. (1996) Model determination using sampling-based methods. In W. Gilks, S. Richardson and D. 145–161, Chapman Hall, London. Gelfand, A. and Dey, D. (1994) Bayesian model choice: Asymptotics and exact calculations. Journal of the Royal Statistical Society B, 56(3), 501–514.

G. inequality indices for smoothed small area mortality rates) (Congdon and Southall, 2005). , 2009). , 2005). g. , 2003; Austin, 2005). Hierarchical procedures for ‘borrowing strength’ or ‘pooling strength’ rest on implicit assumptions: that the units are exchangeable, or similar enough to justify an assumption of a common density (Lindley and Smith, 1972; Bernardo, 1996), and that the hierarchical model chosen is an appropriate one, for example, that a single normal hyperpopulation is appropriate (Marshall and Spiegelhalter, 1998).

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Applied Bayesian Modelling (2nd Edition) (Wiley Series in by Peter D. Congdon
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