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Methods: the model was developed using the WinBUGS software.
After supplying prior distributions for parameters it is straightforward to specify an MCMC algorithm using the WinBUGS software (Spiegelhalter et al. [2000]).
We reviewed Bayesian approaches for animal-level and herd-level prevalence estimation based on cross-sectional sampling designs and demonstrated fitting of these models using the WinBUGS software.
48 Network meta-analysis will be performed using the WinBUGS software package (V.1.4.3, MRC Biostatistics Unit, Cambridge, UK) with random-effects models for multiarm trials.
50 Network meta-analyses will be performed using the WinBUGS software package (V.1.4.3, MRC Biostatistics Unit, Cambridge, UK) with random-effects models for multiarm trials.
When implemented using the WinBUGS software, the missing study effects and standard errors are coded as NA in the data, a strategy previously outlined in Bujkiewicz et al.[ 10] and Dakin et al.[ 28].
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Bayesian estimation and inference are implemented using the Winbugs package via Markov chain Monte Carlo (MCMC) methods (Lunn et al. 2009).
All other components of the model were as described for model A. The NMA analyses were undertaken in the WinBUGs software [ 35].
In order to quantify the uncertainty on production forecast the Bayesian parameter estimation conditioned on historical information was done using the software Winbugs (Spiegelhalter et al. 2000).
To achieve this, components of variation were determined by Bayesian modelling (Markov chain Monte Carlo) using WinBugs software [15], to arrive at estimated confidence interval half-widths for differing future rating situations.
We fitted Bayesian regression models using WinBugs software [ 17], based on a Poisson distribution, and allowed for practice-level variation (over-dispersion) by adding a random-effects term to the transformed rate, following log transformation.
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