# #StackBounty: #expected-value #gamma-distribution #variational-bayes #dirichlet-distribution Using categorical data to build a Dirichle…

### Bounty: 150

I am building a graphical model. I have some categorical data $$boldsymbol{mu}$$ where they are generated by $$p(boldsymbol{mu}|boldsymbol{s},mathbf{A})=prod_kprod_jmathbf{A}_{ij}^{mu_is_j}$$. I’d like to use $$boldsymbol{mu}$$ to be mapped to a Dirichlet distribution $$P(boldsymbol{pi}|boldsymbol{alpha})=mathrm{Dir}(alpha)$$.

I thought I can use the categorical distribution as a base distribution with some $$alpha$$ scalar value. But after investigating more, actually it is not possible because the concentration parameter vector $$boldsymbol{alpha}$$ must be values greater than zero.

Can anybody suggest how I can solve this problem?

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