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Parameters in linear predictor are t-distributed. When the number of records goes to infinity, it converges to normal distribution. So yes, normally it is considered correct to assume normal distribution of parameters.

Anyways, in bayesian statistics, you need not to assume parameter distribution. Normally you specify so called uninformative priors. For each case, different uninformative priors are recommended. In this case, people often use something like (you can tweak the values of course):

dunif(-100000, 100000)
or

dnorm(0, 1/10^10)
The second one is preferred, because it is not limited to particular values. With uninformative priors, you have take no risk. You can of course limit them to particular interval, but be careful.

So, you specify uninformative prior and the parameter distribution will come out itself! No need to make any assumptions about it.

Unfortunately, harmless seeming priors can be very dangerous (and have even fooled some seasoned Bayesians).

This recent paper, provides a nice introduction along with plotting methods to visualize the prior and posterior (usually marginal priors/posterior for the parameter(s) of interest).

Hidden Dangers of Specifying Noninformative Priors. John W. Seaman III, John W. Seaman Jr. & James D. Stamey The American StatisticianVolume 66, Issue 2, May 2012, pages 77-84. http://amstat.tandfonline.com/doi/full/10.1080/00031305.2012.695938

Such plots in my opinion should be obligatory in any actual Bayesian analysis, even if the analyst does not need them – what is happening in a Bayesian analysis should be made clear for most readers.

谢谢你