# Probabilistic comparison of two mixture models?

Given two gaussian mixture models (GMMs) with different degrees of freedom, is there a way to determine the probability that one is generated from the other? That is, can we give a probability to the hypothesis that the two distributions are actually the same?

My current approach is to use a correlation score, which is easy to calculate but is not really a probability:

$$C(p_1,p_2) = -\log \left[ \frac{\int p_1(x)p_2(x)dx}{\int p_1^2(x)+p_2^2(x)dx}\right]$$ For two GMMs $p_1(\boldsymbol r)=\sum_i\pi_i\phi_i(\boldsymbol r|\boldsymbol \mu_i,\boldsymbol \Sigma_i)$ and $p_2(\boldsymbol r)=\sum_j\pi_j\phi_j(\boldsymbol r|\boldsymbol \mu_j,\boldsymbol \Sigma_j)$ we have: $$\int p_1p_2 = \sum_{i,j} \pi_i\pi_j\int\phi_i(\boldsymbol r)\phi_j(\boldsymbol r)d\boldsymbol r$$ The final integral is straightforward to compute because the product of two gaussians is another gaussian. I like this function because it's analytic, but it really doesn't tell us a probability. One additional thought is to use a multivariate t-test but this seems to only be useful for comparing two normal distributions. I need to compare two mixtures with unequal degrees of freedom.