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I am trying to build a Bayesian regression model with LASSO regularization. My understanding is that I can do this by setting a Laplace prior on the coefficients. I also need a prior for the variance of the error. Is there a conjugate prior I can use here, akin to the normal-inverse-Gamma prior for a standard Bayesian regression model with ridge regularization?

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