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We currently only allow specifying inverse gamma priors with finite/unique mean and variance. But Leeper/Walker/Yang (2010) in an influential paper use an inverse gamma prior with s=1 and nu=4 (parametrization as at http://en.wikipedia.org/wiki/Inverse-gamma_distribution), which implies non-existing first and second moments. I would suggest to provide a new prior inv_gamma_parametrized that takes the values provided in the mean and standard deviation fields of the estimated_params-block directly as the parameters of the distribution, thereby avoiding the impossible transformation from mean and variances. I would implement this when doing #520 (closed)