I'm trying to generate a text document using reverse "Correlated Topic Models", which is an advanced version of LDA (Latent Dirichlet Allocation). In this version the topics are generated over a multinomial distribution with a Logistic-Normal prior. This allows adding correlation between topics through the covariance matrix.
Can anyone please help me simulate values from a multivariate Logistic-Normal distirubtion, i.e. given input parameters myu, sigma (mean vector, and covariance matrix respectively), the result is a distribution over topics.