# How Do I choose parameters of prior on regression coefficients in a Bayesian linear model?

I'm trying to perform a linear regression in a Bayesian way.

The response is normal,the prior I would like to put over $\mathbf{\beta}$ (vector of regression coefficients) and $\Sigma^2$ (variance of the error term) is a Normal inverse-gamma one: Pi(Beta,Sig^2)=P(Beta|Sig^2)*P(Sig^2)

$P(\mathbf{\beta}|\Sigma^2) \sim N_p(b,B)$

$P(\Sigma^2) \sim InvGamma(u,U)$

My problem regards the choice of the parameters of the prior($b,B,u,U$).