I’m late to the party here, but I recommend Chapter 5 from Girosi & King’s Demographic Forecasting, particularly section 5.3 where they talk about how they start with priors on \mu to derive corresponding priors on \beta. I’ve used it previously for forecasting and it works well to penalize models that produce unsmooth outputs (rather than just unsmooth parameters).
[I actually even recreated their YourCast model in PyMC2 back in the day, but it is unfortunately only available on some old BitBucket server locked away in the bowels of my former institution…]