Gaussian mixture Gibbs updates with independent priors ($v_i\mid \cdots \sim \mathrm{InvGamma}(\alpha +n_i/2,\beta +\frac{1}{2}{\sum }_{z_j=i}(x_j-{\mu }_i{)}^2)$)

In a finite normal distribution mixture with allocation labels $z_j$, independently assign ${\mu }_i\sim N(m_0,{\tau }^2)$, $v_i\sim \mathrm{InvGamma}(\alpha ,\beta )$ and weights a Dirichlet distribution. Let $n_i$ count allocations to component $i$ and $s_i$ sum their observations. The conditional mean precision is $V_i^{-1}={\tau }^{-2}+n_i/v_i$, giving ${\mu }_i\mid \cdots \sim N(V_i(m_0/{\tau }^2+s_i/v_i),V_i)$. The conditional variance is the displayed inverse-gamma distribution; weights have Dirichlet parameters increased by allocation counts. Allocation probabilities are proportional to ${\omega }_i\phi (x_j;{\mu }_i,v_i)$. Independence of the mean prior from $v_i$ is important: its density does not add an extra inverse-gamma shape term.