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Dear all, I am a little stuck with a particular problem regarding the extrapolation of a linear trend from a set of posterior distributions. I have a Bayesian model which I have simulated in WinBUGS to produce posterior distributions about a vector of 19 parameters which I will call y(1), y(2),... y(19). I also have data pertaining to a "standard" vector of 22 parameters which I will call x(1), x(2),... x(22). My aim is to use a weighted least squares regression to extrapolate beyond y(19) and thus predict y(20), y(21) and y(22) using the linear formula: y(i) = a + b*[x(i)] with a and b obtained from the weighted least squares regression. After this, I will be calculating a single final parameter, z, which is a function of y(1),... y(22) (i.e. the entire y vector). I would like information on the uncertainty of z to be retained. I'm a little stumped as to how to do this. Is it possible to do this directly in WinBUGS within the same model syntax or is it better to take the posterior distributions derived from the first model and perform the extrapolation (and subsequent calculation of z) using different software? Any insights (or useful references) on this would be gratefully received - I will post a synthesis of responses in a couple of days' time. Many thanks, Michael Grayer. -- ================================ Michael Grayer ESRC/CASE PhD Student Department of Geography Queen Mary, University of London London E1 4NS ================================