[精彩WinBUGS答问]How can I estimate the precision of a normal using

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I am trying to estimate the precision τ of a normal distribution with either WinBUGS or OpenBUGS. I tried using a standard approach here by setting τ～gamma(0.001,0.001) but WinBUGS and OpenBUGS both stop with an error message. WinBUGS basically tells me to increase the parameters for the gamma prior.

However, if I do that, the MCMC chains of τ act strangely, even if I run 200.000 iterations withthin=200:

As you can see, the chains have spikes of up to 3000and in other cases even more. I'm not very experienced but I think this shouldn't be the case. The parameter c is in the range of [0;1] so I'm worried about precision estimates of above 400.

This question seems to address my issue. It says that Gibbs samplers (like WinBUGS) have problems estimating the precision of a normal if the parameters of the gamma distribution are close to zero (which is not exactly true for my case, but they are fairly small). Playing around with the parameters of the gamma distribution does help but I thought the whole purpose of using a gamma prior is to approximate a uniform which is close to zero.

I have also tried estimating the precision indirectly by estimating the standard deviation as a uniform distribution first but the result was more or less the same.

Any help is highly appreciated.

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关键词：PRECISION estimate winbugs WINBUG normal normal

had exactly the same with specifying dgamma(0.001, 0.001) for WinBUGS! Jags can actually take it (and I recommend you to use Jags if you don't need advanced WinBUGS features; and even if you want to use WinBUGS, it comes in handy for debugging, because Jags has much more comprehensible error reporting), but for WinBUGS, you must do:

x[..] ~ dnorm(mean[..], tau)

tau ~ dgamma(0.01, 0.01)
Don't be afraid, dgamma is a good choice. You just have to tweak the parameters. Follow this resource which perfectly explains gamma parameters and prior choice! http://www.unc.edu/courses/2010fall/ecol/563/001/docs/lectures/lecture14.htm#precision

Also note that it might come in handy to normalize the input variables - it makes parameter estimation (and prior choice) much more smooth and easier, because the basic priors will work perfectly with the usual constants.