weighted sum of posterior Dirichlet distributions I have the following distribution:
$q(\vec\theta) = \frac{\sum_k \alpha_k}{\sum_k \beta_k \alpha_k} (\sum_k\theta_k\beta_k) \frac{\Gamma(\sum_k \alpha_k)}{\prod_k\Gamma(\alpha_k)} \prod_k \theta_{k}^{\alpha_k-1} = \frac{\sum_k \alpha_k}{\sum_k \beta_k \alpha_k} (\sum_k\theta_k\beta_k) Dir(\vec\theta|\vec\alpha)$
And I want to get the first moment of distribution $q(\vec\theta)$:
$\mathop{\mathbb{E}}_q[\theta_k]=\int_\vec\theta\theta_kq(\vec\theta)d\vec\theta$
My first question is about the limits of the integral. Since I am getting the expectation of $q$, should I integrate w.r.t. $\vec\theta$ or $\theta_k$. I mean $\int_\vec\theta\theta_kq(\vec\theta)d\vec\theta$ or $\int_{\theta_k}\theta_kq(\vec\theta)d\theta_k$.
After simplifying a little bit $q(\vec\theta)$, we get:
$\mathop{\mathbb{E}}_q[\theta_k] = \frac{\sum_k \alpha_k}{\sum_k \beta_k \alpha_k} \int\theta_k(\sum_k\theta_k\beta_k) Dir(\vec\theta|\vec\alpha)d\theta_k = \frac{\sum_k \alpha_k}{\sum_k \beta_k \alpha_k} [\beta_1\int\theta_k\theta_1Dir(\vec\theta|\vec\alpha)d\theta_k + \beta_2\int\theta_k\theta_2Dir(\vec\theta|\vec\alpha)d\theta_k + ... + \beta_k\int\theta_k\theta_kDir(\vec\theta|\vec\alpha)d\theta_k ]$
And this is starting to look as the mean of the Dirichlet Distribution. For instance, $\mathop{\mathbb{E}}_p[X_k] = \frac{\phi_k}{\sum_k\phi_k}$ for $Dir(\vec X|\vec\phi)$. 
Any suggestions or help to 1) solve the integral and 2) verify over what to integrate (i.e. $d\vec\theta$ or $d\theta_k$)

BTW, I got confused with 2) because for $Dir(\vec X|\vec\phi)$ we have a mean $\mathop{\mathbb{E}}_p[X_k] = \frac{\phi_k}{\sum_k\phi_k}$
Or we can put the mean as: $\mathop{\mathbb{E}}_p[X_k] = \int X_k \frac{\Gamma(\sum_k \phi_k)}{\prod_k\Gamma(\phi_k)}\prod_k X_k^{\phi_k-1} d\vec \theta = \frac{\phi_k}{\sum_k\phi_k}$. However, the definition of expectation for a vector rv is: $\mathop{\mathbb{E}}_p[X_k] = \int X_k p(\vec X) dX_k$
 A: In short, the posterior distribution is a mixture of Dirichlet distributions
$$q(\theta)\propto\sum_{k=1}^K \beta_k\theta_k\text{Dir}(\theta;\alpha)\qquad\qquad(1)$$(where I removed the vector notation for $\theta$ and $\alpha$). Since the pdf of a Dirichlet $\text{Dir}(\alpha)$ distribution is$$\frac{1}{\mathrm{B}(\alpha)} \prod_{i=1}^K x_i^{\alpha_i - 1}$$where$$\mathrm{B}(\alpha) = \frac{\prod_{i=1}^K \Gamma(\alpha_i)}{\Gamma\bigl(\sum_{i=1}^K \alpha_i\bigr)}$$
the integral of the term $\theta_k\text{Dir}(\theta;\alpha)$ in (1) is $\alpha_k/\mathbf{1}^T\alpha$ and hence the normalising constant of this density $q$ is indeed$$\mathbf{1}^T\alpha\Big/\sum_{k=1}^K \beta_k\alpha_k$$ and the weight of the $\text{Dir}(\theta;\alpha_1,\ldots,\alpha_k+1,\ldots,\alpha_K)$ term in the mixture is
$$\text{B}(\alpha_1,\ldots,\alpha_k+1,\ldots,\alpha_K))/\text{B}(\alpha)=\frac{\alpha_k\prod_{i=1}^K \Gamma(\alpha_i)}{\{\mathbf{1}^T\alpha\}\Gamma\bigl(\sum_{i=1}^K \alpha_i\bigr)}\Big/\frac{\prod_{i=1}^K \Gamma(\alpha_i)}{\Gamma\bigl(\sum_{i=1}^K \alpha_i\bigr)}=\frac{\alpha_k}{\{\mathbf{1}^T\alpha\}}$$hence$$q(\theta)=\sum_{k=1}^K \beta_k\alpha_k\text{Dir}(\theta;\alpha_1,\ldots,\alpha_k+1,\ldots,\alpha_K)\Big/\sum_{k=1}^K \beta_k\alpha_k$$The mean of a component $\theta_j$ of $\theta$ is thus given by
$$\mathbb{E}_q[\theta_j]=\sum_{k=1}^K \beta_k\alpha_k \mathbb{E}[\theta_j|
\alpha_1,\ldots,\alpha_k+1,\ldots,\alpha_K]\Big/\sum_{k=1}^K \beta_k\alpha_k$$namely
$$\mathbb{E}_q[\theta_j]=\left\{\sum_{k\ne j} \beta_k\alpha_k\frac{\alpha_j}{\{\mathbf{1}^T\alpha\}+1}+\beta_j\alpha_j\frac{\alpha_j+1}{\{\mathbf{1}^T\alpha\}+1}\right\}\Big/\sum_{k=1}^K \beta_k\alpha_k$$ 
The specific questions about the integration over $\theta$ versus the integration over $\theta_k$ relate to the so-called "law of the unconscious statistician" in that the marginal of $\theta_k$ (which would be a mixture of Beta distributions in the current case) need not be derived for the expectation to be found.
